Support Matrix for NIMs#

This page lists supported models, deployment profiles, and verified hardware SKUs for NIM LLM.

Use the following sections to identify the supported deployment profiles for each model. Profile strings follow a naming convention described in Model Profiles and Selection.

Important

The list of NIM Certified models is available on the NGC Catalog site.

Use the table below to filter NIM profiles by GPU, tensor parallelism (TP), precision, LoRA support, and model name. Each row is one supported profile; details also appear in the per-model sections that follow.

Note

The GB300-WS GPU is more commonly known as DGX Station.

LoRA only

Model	TP	Precision	LoRA
gpt-oss-120b	1	MXFP4	No
gpt-oss-120b	2	MXFP4	No
gpt-oss-120b	4	MXFP4	No
gpt-oss-120b	8	MXFP4	No
gpt-oss-120b	1	MXFP4	Yes
gpt-oss-120b	2	MXFP4	Yes
gpt-oss-120b	4	MXFP4	Yes
gpt-oss-120b	8	MXFP4	Yes
gpt-oss-20b	1	MXFP4	No
gpt-oss-20b	2	MXFP4	No
gpt-oss-20b	4	MXFP4	No
gpt-oss-20b	8	MXFP4	No
gpt-oss-20b	1	MXFP4	Yes
gpt-oss-20b	2	MXFP4	Yes
gpt-oss-20b	4	MXFP4	Yes
gpt-oss-20b	8	MXFP4	Yes
llama-3.1-70b-instruct	1	BF16	No
llama-3.1-70b-instruct	2	BF16	No
llama-3.1-70b-instruct	4	BF16	No
llama-3.1-70b-instruct	8	BF16	No
llama-3.1-70b-instruct	1	BF16	Yes
llama-3.1-70b-instruct	2	BF16	Yes
llama-3.1-70b-instruct	4	BF16	Yes
llama-3.1-70b-instruct	8	BF16	Yes
llama-3.1-70b-instruct	1	FP8	No
llama-3.1-70b-instruct	2	FP8	No
llama-3.1-70b-instruct	4	FP8	No
llama-3.1-70b-instruct	8	FP8	No
llama-3.1-70b-instruct	1	FP8	Yes
llama-3.1-70b-instruct	2	FP8	Yes
llama-3.1-70b-instruct	4	FP8	Yes
llama-3.1-70b-instruct	8	FP8	Yes
llama-3.1-70b-instruct	1	NVFP4	No
llama-3.1-70b-instruct	2	NVFP4	No
llama-3.1-70b-instruct	4	NVFP4	No
llama-3.1-70b-instruct	8	NVFP4	No
llama-3.1-70b-instruct	1	NVFP4	Yes
llama-3.1-70b-instruct	2	NVFP4	Yes
llama-3.1-70b-instruct	4	NVFP4	Yes
llama-3.1-70b-instruct	8	NVFP4	Yes
llama-3.1-8b-instruct	1	BF16	No
llama-3.1-8b-instruct	1	BF16	Yes
llama-3.1-8b-instruct	1	FP8	No
llama-3.1-8b-instruct	1	FP8	Yes
llama-3.1-8b-instruct	1	NVFP4	No
llama-3.1-8b-instruct	1	NVFP4	Yes
llama-3.3-70b-instruct	1	BF16	No
llama-3.3-70b-instruct	2	BF16	No
llama-3.3-70b-instruct	4	BF16	No
llama-3.3-70b-instruct	8	BF16	No
llama-3.3-70b-instruct	1	BF16	Yes
llama-3.3-70b-instruct	2	BF16	Yes
llama-3.3-70b-instruct	4	BF16	Yes
llama-3.3-70b-instruct	8	BF16	Yes
llama-3.3-70b-instruct	1	FP8	No
llama-3.3-70b-instruct	2	FP8	No
llama-3.3-70b-instruct	4	FP8	No
llama-3.3-70b-instruct	8	FP8	No
llama-3.3-70b-instruct	1	FP8	Yes
llama-3.3-70b-instruct	2	FP8	Yes
llama-3.3-70b-instruct	4	FP8	Yes
llama-3.3-70b-instruct	8	FP8	Yes
llama-3.3-70b-instruct	1	NVFP4	No
llama-3.3-70b-instruct	2	NVFP4	No
llama-3.3-70b-instruct	4	NVFP4	No
llama-3.3-70b-instruct	8	NVFP4	No
llama-3.3-70b-instruct	1	NVFP4	Yes
llama-3.3-70b-instruct	2	NVFP4	Yes
llama-3.3-70b-instruct	4	NVFP4	Yes
llama-3.3-70b-instruct	8	NVFP4	Yes
llama-3.3-nemotron-super-49b-v1.5	1	BF16	No
llama-3.3-nemotron-super-49b-v1.5	2	BF16	No
llama-3.3-nemotron-super-49b-v1.5	4	BF16	No
llama-3.3-nemotron-super-49b-v1.5	8	BF16	No
llama-3.3-nemotron-super-49b-v1.5	1	BF16	Yes
llama-3.3-nemotron-super-49b-v1.5	2	BF16	Yes
llama-3.3-nemotron-super-49b-v1.5	4	BF16	Yes
llama-3.3-nemotron-super-49b-v1.5	8	BF16	Yes
llama-3.3-nemotron-super-49b-v1.5	1	FP8	No
llama-3.3-nemotron-super-49b-v1.5	2	FP8	No
llama-3.3-nemotron-super-49b-v1.5	4	FP8	No
llama-3.3-nemotron-super-49b-v1.5	8	FP8	No
llama-3.3-nemotron-super-49b-v1.5	1	FP8	Yes
llama-3.3-nemotron-super-49b-v1.5	2	FP8	Yes
llama-3.3-nemotron-super-49b-v1.5	4	FP8	Yes
llama-3.3-nemotron-super-49b-v1.5	8	FP8	Yes
llama-3.3-nemotron-super-49b-v1.5	1	NVFP4	No
llama-3.3-nemotron-super-49b-v1.5	2	NVFP4	No
llama-3.3-nemotron-super-49b-v1.5	4	NVFP4	No
llama-3.3-nemotron-super-49b-v1.5	8	NVFP4	No
llama-3.3-nemotron-super-49b-v1.5	1	NVFP4	Yes
llama-3.3-nemotron-super-49b-v1.5	2	NVFP4	Yes
llama-3.3-nemotron-super-49b-v1.5	4	NVFP4	Yes
llama-3.3-nemotron-super-49b-v1.5	8	NVFP4	Yes
nemotron-content-safety-reasoning-4b-experimental	1	BF16	No
nemotron-content-safety-reasoning-4b-experimental	2	BF16	No
nemotron-content-safety-reasoning-4b-experimental	4	BF16	No
nemotron-content-safety-reasoning-4b-experimental	8	BF16	No
nemotron-3-nano	1	BF16	No
nemotron-3-nano	2	BF16	No
nemotron-3-nano	4	BF16	No
nemotron-3-nano	8	BF16	No
nemotron-3-nano	1	BF16	Yes
nemotron-3-nano	2	BF16	Yes
nemotron-3-nano	4	BF16	Yes
nemotron-3-nano	8	BF16	Yes
nemotron-3-nano	1	FP8	No
nemotron-3-nano	2	FP8	No
nemotron-3-nano	4	FP8	No
nemotron-3-nano	8	FP8	No
nemotron-3-nano	1	NVFP4	No
nemotron-3-nano	2	NVFP4	No
nemotron-3-nano	4	NVFP4	No
nemotron-3-nano	8	NVFP4	No
nemotron-3-nano	1	NVFP4	Yes
nemotron-3-super-120b-a12b	1	BF16	No
nemotron-3-super-120b-a12b	2	BF16	No
nemotron-3-super-120b-a12b	4	BF16	No
nemotron-3-super-120b-a12b	8	BF16	No
nemotron-3-super-120b-a12b	2	BF16	Yes
nemotron-3-super-120b-a12b	4	BF16	Yes
nemotron-3-super-120b-a12b	8	BF16	Yes
nemotron-3-super-120b-a12b	1	FP8	No
nemotron-3-super-120b-a12b	2	FP8	No
nemotron-3-super-120b-a12b	4	FP8	No
nemotron-3-super-120b-a12b	8	FP8	No
nemotron-3-super-120b-a12b	1	NVFP4	No
nemotron-3-super-120b-a12b	2	NVFP4	No
nemotron-3-super-120b-a12b	4	NVFP4	No
nemotron-3-super-120b-a12b	8	NVFP4	No
nemotron-3-super-120b-a12b	1	NVFP4	Yes
nemotron-3-super-120b-a12b	2	NVFP4	Yes
nemotron-3-ultra-550b-a55b	8	BF16	No
nemotron-3-ultra-550b-a55b	2	NVFP4	No
nemotron-3-ultra-550b-a55b	4	NVFP4	No
nemotron-3-ultra-550b-a55b	8	NVFP4	No
nemotron-3.5-lightning-30b-a3b	1	BF16	No
nemotron-3.5-lightning-30b-a3b	1	BF16	Yes
nemotron-3.5-lightning-30b-a3b	2	BF16	No
nemotron-3.5-lightning-30b-a3b	2	BF16	Yes
nemotron-3.5-lightning-30b-a3b	4	BF16	No
nemotron-3.5-lightning-30b-a3b	4	BF16	Yes
nemotron-3.5-lightning-30b-a3b	8	BF16	No
nemotron-3.5-lightning-30b-a3b	8	BF16	Yes
nemotron-3.5-lightning-30b-a3b	1	W4A16	No
nemotron-3.5-lightning-30b-a3b	1	W4A16	Yes
nemotron-3.5-lightning-30b-a3b	2	W4A16	No
nemotron-3.5-lightning-30b-a3b	2	W4A16	Yes
nemotron-3.5-lightning-30b-a3b	4	W4A16	No
nemotron-3.5-lightning-30b-a3b	4	W4A16	Yes
nemotron-3.5-lightning-30b-a3b	1	NVFP4	No
nemotron-3.5-lightning-30b-a3b	1	NVFP4	Yes
nemotron-3.5-lightning-30b-a3b	2	NVFP4	No
nemotron-3.5-lightning-30b-a3b	2	NVFP4	Yes
nemotron-3.5-lightning-30b-a3b	4	NVFP4	No
nemotron-3.5-lightning-30b-a3b	4	NVFP4	Yes
riva-translate-4b-instruct-v2	1	BF16	No
starcoder2-7b	1	BF16	No
starcoder2-7b	2	BF16	No
No matching profiles. This configuration may still be deployable with memory tuning; refer to the memory troubleshooting guide for details.
No matching profiles. This GPU is verified for Model-Free NIM deployment, and this configuration may also be deployable with memory tuning; refer to the memory troubleshooting guide for details.

gpt-oss-120b#

The following table lists the supported profile configurations for openai/gpt-oss-120b:

Precision	TP1	TP2	TP4	TP8
MXFP4	`vllm-mxfp4-tp1-pp1`	`vllm-mxfp4-tp2-pp1`	`vllm-mxfp4-tp4-pp1`	`vllm-mxfp4-tp8-pp1`
MXFP4 + LoRA	`vllm-mxfp4-tp1-pp1-lora`	`vllm-mxfp4-tp2-pp1-lora`	`vllm-mxfp4-tp4-pp1-lora`	`vllm-mxfp4-tp8-pp1-lora`

Note

Support requirements in 2.0.11: The MXFP4 TP8 profile on NVIDIA A10G and NVIDIA A100-SXM4-40GB requires NIM_KVCACHE_PERCENT=0.80. The LoRA TP4 and TP8 profiles on NVIDIA L40S and NVIDIA RTX PRO 4500 Blackwell Server Edition require piecewise CUDA graphs. LoRA adapters with rank greater than 128 are not supported.

gpt-oss-20b#

The following table lists the supported profile configurations for openai/gpt-oss-20b:

Precision	TP1	TP2	TP4	TP8
MXFP4	`vllm-mxfp4-tp1-pp1`	`vllm-mxfp4-tp2-pp1`	`vllm-mxfp4-tp4-pp1`	`vllm-mxfp4-tp8-pp1`
MXFP4 + LoRA	`vllm-mxfp4-tp1-pp1-lora`	`vllm-mxfp4-tp2-pp1-lora`	`vllm-mxfp4-tp4-pp1-lora`	`vllm-mxfp4-tp8-pp1-lora`

Note

Support requirements in 2.0.11: LoRA adapters with rank greater than 128 are not supported. On NVIDIA A10G, LoRA deployments require NIM_MAX_MODEL_LEN below the default 131,072 token context length so that the KV cache fits in device memory.

llama-3.1-70b-instruct#

The following table lists the supported profile configurations for meta/llama-3.1-70b-instruct:

Precision	TP1	TP2	TP4	TP8
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`	`vllm-bf16-tp4-pp1`	`vllm-bf16-tp8-pp1`
BF16 + LoRA	`vllm-bf16-tp1-pp1-lora`	`vllm-bf16-tp2-pp1-lora`	`vllm-bf16-tp4-pp1-lora`	`vllm-bf16-tp8-pp1-lora`
FP8	`vllm-fp8-tp1-pp1`	`vllm-fp8-tp2-pp1`	`vllm-fp8-tp4-pp1`	`vllm-fp8-tp8-pp1`
FP8 + LoRA	`vllm-fp8-tp1-pp1-lora`	`vllm-fp8-tp2-pp1-lora`	`vllm-fp8-tp4-pp1-lora`	`vllm-fp8-tp8-pp1-lora`
NVFP4	`vllm-nvfp4-tp1-pp1`	`vllm-nvfp4-tp2-pp1`	`vllm-nvfp4-tp4-pp1`	`vllm-nvfp4-tp8-pp1`
NVFP4 + LoRA	`vllm-nvfp4-tp1-pp1-lora`	`vllm-nvfp4-tp2-pp1-lora`	`vllm-nvfp4-tp4-pp1-lora`	`vllm-nvfp4-tp8-pp1-lora`

Note

Support change in 2.0.11: BF16 TP2 with or without LoRA is not supported on NVIDIA RTX PRO 6000 Blackwell Server Edition, and BF16 TP8 without LoRA is not supported on NVIDIA B200. These combinations are omitted from the profile-to-GPU map above.

llama-3.1-8b-instruct#

The following table lists the supported profile configurations for meta/llama-3.1-8b-instruct:

Precision	TP1
BF16	`vllm-bf16-tp1-pp1`
BF16 + LoRA	`vllm-bf16-tp1-pp1-lora`
FP8	`vllm-fp8-tp1-pp1`
FP8 + LoRA	`vllm-fp8-tp1-pp1-lora`
NVFP4	`vllm-nvfp4-tp1-pp1`
NVFP4 + LoRA	`vllm-nvfp4-tp1-pp1-lora`

llama-3.3-70b-instruct#

The following table lists the supported profile configurations for meta/llama-3.3-70b-instruct:

Precision	TP1	TP2	TP4	TP8
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`	`vllm-bf16-tp4-pp1`	`vllm-bf16-tp8-pp1`
BF16 + LoRA	`vllm-bf16-tp1-pp1-lora`	`vllm-bf16-tp2-pp1-lora`	`vllm-bf16-tp4-pp1-lora`	`vllm-bf16-tp8-pp1-lora`
FP8	`vllm-fp8-tp1-pp1`	`vllm-fp8-tp2-pp1`	`vllm-fp8-tp4-pp1`	`vllm-fp8-tp8-pp1`
FP8 + LoRA	`vllm-fp8-tp1-pp1-lora`	`vllm-fp8-tp2-pp1-lora`	`vllm-fp8-tp4-pp1-lora`	`vllm-fp8-tp8-pp1-lora`
NVFP4	`vllm-nvfp4-tp1-pp1`	`vllm-nvfp4-tp2-pp1`	`vllm-nvfp4-tp4-pp1`	`vllm-nvfp4-tp8-pp1`
NVFP4 + LoRA	`vllm-nvfp4-tp1-pp1-lora`	`vllm-nvfp4-tp2-pp1-lora`	`vllm-nvfp4-tp4-pp1-lora`	`vllm-nvfp4-tp8-pp1-lora`

llama-3.3-nemotron-super-49b-v1.5#

The following table lists the supported profile configurations for nvidia/llama-3.3-nemotron-super-49b-v1.5:

Precision	TP1	TP2	TP4	TP8
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`	`vllm-bf16-tp4-pp1`	`vllm-bf16-tp8-pp1`
BF16 + LoRA	`vllm-bf16-tp1-pp1-lora`	`vllm-bf16-tp2-pp1-lora`	`vllm-bf16-tp4-pp1-lora`	`vllm-bf16-tp8-pp1-lora`
FP8	`vllm-fp8-tp1-pp1`	`vllm-fp8-tp2-pp1`	`vllm-fp8-tp4-pp1`	`vllm-fp8-tp8-pp1`
FP8 + LoRA	`vllm-fp8-tp1-pp1-lora`	`vllm-fp8-tp2-pp1-lora`	`vllm-fp8-tp4-pp1-lora`	`vllm-fp8-tp8-pp1-lora`
NVFP4	`vllm-nvfp4-tp1-pp1`	`vllm-nvfp4-tp2-pp1`	`vllm-nvfp4-tp4-pp1`	`vllm-nvfp4-tp8-pp1`
NVFP4 + LoRA	`vllm-nvfp4-tp1-pp1-lora`	`vllm-nvfp4-tp2-pp1-lora`	`vllm-nvfp4-tp4-pp1-lora`	`vllm-nvfp4-tp8-pp1-lora`

nemotron-content-safety-reasoning-4b-experimental#

The following table lists the supported profile configurations for nvidia/nemotron-content-safety-reasoning-4b-experimental. The latest release version of this NIM is 2.0.3.

Precision	TP1	TP2	TP4	TP8
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`	`vllm-bf16-tp4-pp1`	`vllm-bf16-tp8-pp1`

nemotron-3-nano#

The following table lists the supported profile configurations for nvidia/nemotron-3-nano:

Precision	TP1	TP2	TP4	TP8
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`	`vllm-bf16-tp4-pp1`	`vllm-bf16-tp8-pp1`
BF16 + LoRA	`vllm-bf16-tp1-pp1-lora`	`vllm-bf16-tp2-pp1-lora`	`vllm-bf16-tp4-pp1-lora`	`vllm-bf16-tp8-pp1-lora`
FP8	`vllm-fp8-tp1-pp1`	`vllm-fp8-tp2-pp1`	`vllm-fp8-tp4-pp1`	`vllm-fp8-tp8-pp1`
NVFP4	`vllm-nvfp4-tp1-pp1`	`vllm-nvfp4-tp2-pp1`	`vllm-nvfp4-tp4-pp1`	`vllm-nvfp4-tp8-pp1`
NVFP4 + LoRA	`vllm-nvfp4-tp1-pp1-lora`	–	–	–

Note

Support requirement in 2.0.11: The fused mixture-of-experts kernels support LoRA adapters with a maximum rank of 128. Adapters with a greater rank are not supported.

nemotron-3-super-120b-a12b#

The following table lists the supported profile configurations for nvidia/nemotron-3-super-120b-a12b:

Precision	TP1	TP2	TP4	TP8
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`	`vllm-bf16-tp4-pp1`	`vllm-bf16-tp8-pp1`
BF16 + LoRA	–	`vllm-bf16-tp2-pp1-lora`	`vllm-bf16-tp4-pp1-lora`	`vllm-bf16-tp8-pp1-lora`
FP8	`vllm-fp8-tp1-pp1`	`vllm-fp8-tp2-pp1`	`vllm-fp8-tp4-pp1`	`vllm-fp8-tp8-pp1`
NVFP4	`vllm-nvfp4-tp1-pp1`	`vllm-nvfp4-tp2-pp1`	`vllm-nvfp4-tp4-pp1`	`vllm-nvfp4-tp8-pp1`
NVFP4 + LoRA	`vllm-nvfp4-tp1-pp1-lora`	`vllm-nvfp4-tp2-pp1-lora`	–	–

Note

This is a large model. Lower-TP profiles require substantially more GPU memory per device, so some verified GPUs support only TP4 or TP8 profiles.

Note

Support change in 2.0.11: The following BF16 LoRA combinations are not supported: TP2 on NVIDIA GH200-144G-HBM3e and NVIDIA H200-NVL, TP4 on NVIDIA H100-NVL, and TP8 on NVIDIA L40S. These combinations are omitted from the profile-to-GPU map above.

nemotron-3-ultra-550b-a55b#

The following table lists the supported profile configurations for nvidia/nemotron-3-ultra-550b-a55b:

Precision	TP1	TP2	TP4	TP8
BF16	–	–	–	`vllm-bf16-tp8-pp1`
BF16 + LoRA	–	–	–	–
NVFP4	–	`vllm-nvfp4-tp2-pp1`	`vllm-nvfp4-tp4-pp1`	`vllm-nvfp4-tp8-pp1`

Note

The minimum deployment is two GPUs, using vllm-nvfp4-tp2-pp1 on high-memory Blackwell SKUs (NVIDIA-B300-SXM6-AC and NVIDIA-GB300); the other profiles use four or eight GPUs. H200 validation covers one NVFP4 TP4 profile and is less extensive than the Blackwell validation.

FP8 profiles were not released for this NIM because NVFP4 profiles work on all tested Hopper-architecture GPUs (for example, H100 and H200) and deliver better performance than FP8 profiles.

nemotron-3.5-lightning-30b-a3b#

The following table lists the supported profile configurations for nvidia/nemotron-3.5-lightning-30b-a3b, along with the minimum GPU VRAM required per device and the GPU architecture that each precision requires. The VRAM values come from the profile disk-sizing metadata reported by list-model-profiles and represent the minimum per-GPU memory needed to load the model weights and the runtime allocations; they do not include additional headroom for large KV caches at high context length or high concurrency.

The latest release version of this NIM is 2.0.10.

Nemotron 3.5 Lightning Profile Information#
Precision	TP	PP	Min VRAM per GPU	Min GPU count	Architecture requirement	LoRA Profile
BF16	1	1	66 GB	1	Ampere or newer (SM 8.0+)	Base and LoRA
BF16	2	1	35 GB	2	Ampere or newer (SM 8.0+)	Base and LoRA
BF16	4	1	20 GB	4	Ampere or newer (SM 8.0+)	Base and LoRA
BF16	8	1	12 GB	8	Ampere or newer (SM 8.0+)	Base and LoRA
W4A16	1	1	32 GB	1	Ampere or newer (SM 8.0+)	Base and LoRA
W4A16	2	1	20 GB	2	Ampere or newer (SM 8.0+)	Base and LoRA
W4A16	4	1	14 GB	4	Ampere or newer (SM 8.0+)	Base and LoRA
NVFP4	1	1	30 GB	1	Blackwell or newer (SM 10.0+)	Base and LoRA
NVFP4	2	1	18 GB	2	Blackwell or newer (SM 10.0+)	Base and LoRA
NVFP4	4	1	12 GB	4	Blackwell or newer (SM 10.0+)	Base and LoRA

Notes:

Min VRAM per GPU is the floor that list-model-profiles reports for that precision and tensor-parallel size. A GPU below the floor is filtered out during profile selection.
Architecture requirement reflects the kernels each precision needs. NVFP4 requires Streaming Multiprocessor (SM) 10.0+ (Blackwell) because it uses NVFP4-native tensor cores. BF16 and W4A16 run on any Ampere-class or newer GPU (SM 8.0+).
Sharing weights across more GPUs (higher TP) lowers the per-GPU VRAM floor and enables lower-memory GPUs.
Model-cache disk sizes range from approximately 19 GB (NVFP4) to approximately 63 GB (BF16). Cache size is separate from GPU VRAM and does not indicate that the model will fit in a given GPU.

Verified GPUs

This model has been verified on the following GPUs (grouped by architecture):

Blackwell (SM 10.0) — supports NVFP4, W4A16, BF16:

GPU	VRAM per GPU	Profiles that fit
`NVIDIA-B200`	180 GB	All profiles
`NVIDIA-B300-SXM6-AC`	288 GB	All profiles
`NVIDIA-GB200`	186 GB	All profiles
`NVIDIA-GB300`	288 GB	All profiles
`NVIDIA-GB10`	128 GB (unified memory)	All profiles; use `--gpu-memory-utilization 0.75` on GB10 (see the getting started guide)
`NVIDIA-RTX-PRO-6000-Blackwell-Server-Edition`	96 GB	All profiles

Hopper (SM 9.0) — supports W4A16 and BF16 (no NVFP4):

GPU	VRAM per GPU	Profiles that fit
`NVIDIA-H100-80GB-HBM3`	80 GB	BF16 all sizes (needs `--max-num-seqs 512` on TP=1), W4A16 all sizes
`NVIDIA-H200`	141 GB	BF16 all sizes, W4A16 all sizes

Ampere (SM 8.0) — supports W4A16 and BF16 (no NVFP4):

GPU	VRAM per GPU	Profiles that fit
`NVIDIA-A100-SXM4-80GB`	80 GB	BF16 TP≥2, W4A16 all sizes

Ada (SM 8.9) — supports W4A16 and BF16 (no NVFP4):

GPU	VRAM per GPU	Profiles that fit
`NVIDIA-L40S`	48 GB	BF16 TP≥2, W4A16 all sizes

Unverified but potentially compatible. Any GPU that meets the per-profile minimum VRAM floor and the architecture requirement in the profile table can run that profile technically, even if it is not in the preceding Verified GPUs tables. Only the GPUs in those tables have been tested end-to-end for this model at this NIM version; untested systems may exhibit different startup, throughput, or accuracy behavior.

riva-translate-4b-instruct-v2#

The following table lists the supported profile configuration for nvidia/riva-translate-4b-instruct-v2. The latest release version of this NIM is 2.0.8.

Precision	TP	PP	LoRA	Profile
BF16	1	1	No	`vllm-bf16-tp1-pp1`

starcoder2-7b#

The following table lists the supported profile configurations for bigcode/starcoder2-7b:

Precision	TP1	TP2
BF16	`vllm-bf16-tp1-pp1`	`vllm-bf16-tp2-pp1`

Model-Free NIM#

The following models are tested and validated for nvidia/model-free-nim:

gpt-oss-20b
apriel-nemotron
codestral

While not explicitly validated, the model-free NIM can be used with any model supported by the underlying backend (vLLM) version. Refer to Model-Free NIM for deployment details.

Note

Model-specific VRAM requirement in 2.0.11: When this NIM serves apriel-nemotron or codestral, use a GPU with at least 80 GB of usable VRAM. These models require approximately 66 GB for weights and runtime allocations and cannot start on smaller GPUs. Other validated models can use the remaining verified GPUs listed above.

1.x NIM LLM Models#

For more information on version 1.x NIMs, refer to the 1.15 version of the NIM LLM Supported Models page.

Show 1.x models

Model (Hardware Requirements)	Organization/Model ID (Catalog Page)
Bielik 11B v2.3 Instruct	`speakleash/bielik-11b-v2.3-instruct`
Code Llama 13B Instruct	`meta/codellama-13b-instruct`
Code Llama 34B Instruct	`meta/codellama-34b-instruct`
Code Llama 70B Instruct	`meta/codellama-70b-instruct`
DeepSeek Coder V2 Lite Instruct	`deepseek-ai/deepseek-coder-v2-lite-instruct`
DeepSeek R1	`deepseek-ai/deepseek-r1`
DeepSeek R1 Distill Llama 8B	`deepseek-ai/deepseek-r1-distill-llama-8b`
DeepSeek R1 Distill Llama 70B	`deepseek-ai/deepseek-r1-distill-llama-70b`
DeepSeek R1 Distill Llama 8B RTX	`deepseek-ai/deepseek-r1-distill-llama-8b`
DeepSeek R1 Distill Qwen 32B	`deepseek-ai/deepseek-r1-distill-qwen-32b`
DeepSeek-V3.1-Terminus	`deepseek-ai/deepseek-v3.1-terminus`
DeepSeek-V3.2-Exp	`deepseek-ai/deepseek-v32-exp-nim`
DeepSeek-V4-Flash	`deepseek-ai/deepseek-v4-flash`
DeepSeek-V4-Flash-0731	`deepseek-ai/deepseek-v4-flash-0731`
DeepSeek-V4-Pro	`deepseek-ai/deepseek-v4-pro`
EuroLLM 9B Instruct	`utter-project/eurollm-9b-instruct`
Gemma 2 2B	`google/gemma-2-2b-instruct`
Gemma 2 9B	`google/gemma-2-9b-it`
Gemma2 9B CPT Sahabat-AI v1 Instruct	`gotocompany/gemma2-9b-cpt-sahabatai-v1-instruct`
Gemma 3 1B Instruct	`google/gemma-3-1b-it`
GLM-5	`zai-org/glm-5`
GLM-5.1	`zai-org/glm-51`
GLM-5.2	`zai-org/glm-5.2`
GPT-OSS-20B	`openai/gpt-oss-20b`
GPT-OSS-120B	`openai/gpt-oss-120b`
Granite 3.3 8B Instruct	`ibm-granite/granite-3.3-8b-instruct`
GreenMind Medium 14B R1	`greennode/greenmind-medium-14b-r1`
Kanana 1.5 8B Instruct 2505	`kakaocorp/kanana-1.5-8b-instruct-2505`
(Meta) Llama 2 7B Chat	`meta/llama-2-7b-chat`
(Meta) Llama 2 13B Chat	`meta/llama-2-13b-chat`
(Meta) Llama 2 70B Chat	`meta/llama-2-70b-chat`
Llama 3 SQLCoder 8B	`defog/llama-3-sqlcoder-8b`
Llama 3 Swallow 70B Instruct V0.1	`tokyotech-llm/llama-3-swallow-70b-instruct-v0.1`
Llama 3 Taiwan 70B Instruct	`yentinglin/llama-3-taiwan-70b-instruct`
Llama 3.1 8B Base	`meta/llama-3.1-8b-base`
Llama 3.1 8B Instruct	`meta/llama-3.1-8b-instruct`
Llama-3.1-8b-Instruct-DGX-Spark	`meta/llama-3.1-8b-instruct-dgx-spark`
Llama-3.1-8B-Instruct PB 25h2	`meta/llama-3.1-8b-instruct-pb25h2`
Llama 3.1 8B Instruct RTX	`meta/llama-3.1-8b-instruct`
Llama 3.1 70B Instruct	`meta/llama-3.1-70b-instruct`
Llama-3.1-70B-Instruct PB 25h2	`meta/llama-3.1-70b-instruct-pb25h2`
Llama 3.1 405B Instruct	`meta/llama-3.1-405b-instruct`
Llama 3.1 Nemotron Nano 4B V1.1	`nvidia/llama3.1-nemotron-nano-4b-v1.1`
Llama 3.1 Nemotron Nano 8B V1	`nvidia/llama-3.1-nemotron-nano-8b-v1`
Llama-3.1-Nemotron-Nano-8B-Healthcare-Text2sql-v1.0	`nvidia/llama-3.1-nemotron-nano-8b-healthcare-text2sql-v1.0`
Llama 3.1 Nemotron Ultra 253B V1	`nvidia/llama-3.1-nemotron-ultra-253b-v1`
Llama 3.1 Nemotron 70B Instruct	`nvidia/llama-3.1-nemotron-70b-instruct`
Llama 3.1 Swallow 8B Instruct v0.1	`tokyotech-llm/llama-3.1-swallow-8b-instruct-v0.1`
Llama 3.1 Swallow 70B Instruct v0.1	`tokyotech-llm/llama-3.1-swallow-70b-instruct-v0.1`
Llama 3.1 Typhoon 2 8B Instruct	`scb10x/llama3.1-typhoon2-8b-instruct`
Llama 3.1 Typhoon 2 70B Instruct	`scb10x/llama-3.1-typhoon2-70b-instruct`
Llama 3.2 1B Instruct	`meta/llama-3.2-1b-instruct`
Llama 3.2 3B Instruct	`meta/llama-3.2-3b-instruct`
Llama 3.3 70B Instruct	`meta/llama-3.3-70b-instruct`
Llama 3.3 Nemotron Super 49B V1	`nvidia/llama-3.3-nemotron-super-49b-v1`
Llama-3.3-Nemotron-Super-49B-v1.5	`nvidia/llama-3.3-nemotron-super-49b-v1.5`
Llama-3.3-Nemotron-Super-49B-Healthcare-Text2sql-v1.0	`nvidia/llama-3.3-nemotron-super-49b-healthcare-text2sql-v1.0`
Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2	`nvidia/llama-3.3-nemotron-super-49b-v1.5-pb25h2`
Meta Llama 3 8B Instruct	`meta/llama3-8b-instruct`
Meta Llama 3 70B Instruct	`meta/llama3-70b-instruct`
Muse Glimmer	`meta/muse-glimmer`
MiMo-V2-Flash	`xiaomi/mimo-v2-flash-experimental`
MiniMax-M2.5	`minimax-ai/minimax-m25`
MiniMax-M2.7	`minimax-ai/minimax-m27`
Mistral 7B Instruct V0.3	`mistralai/mistral-7b-instruct-v0.3`
Mistral NeMo 12B Instruct RTX	`nv-mistralai/mistral-nemo-12b-instruct`
Mistral NeMo 12B Instruct	`nv-mistralai/mistral-nemo-12b-instruct`
Mistral NeMo Minitron 8B 8K Instruct	`nv-mistralai/mistral-nemo-minitron-8b-8k-instruct`
Mistral Small 24b Instruct 2501	`mistralai/mistral-small-24b-instruct-2501`
Mixtral 8x7B Instruct V0.1	`mistralai/mixtral-8x7b-instruct-v0-1`
Mixtral 8x22B Instruct V0.1	`mistralai/mixtral-8x22b-instruct-v01`
Nemotron 4 340B Instruct	`nvidia/nemotron-4-340b-instruct`
Nemotron 4 340B Reward	`nvidia/nemotron-4-340b-reward`
NVIDIA Nemotron 3 Nano	`nvidia/nemotron-3-nano`
NVIDIA-Nemotron-Nano-9B-v2	`nvidia/nvidia-nemotron-nano-9b-v2`
NVIDIA-Nemotron-Nano-9B-v2-DGX-Spark	`nvidia/nvidia-nemotron-nano-9b-v2-dgx-spark`
Nemotron-3-Super-120B-A12B	`nvidia/nemotron-3-super-120b-a12b`
Phi 3 Mini 4K Instruct	`microsoft/phi-3-mini-4k-instruct`
Phi 4 Mini Instruct	`microsoft/phi-4-mini-instruct`
Phind Codellama 34B V2 Instruct	`phind/phind-codellama-34b-v2-instruct`
Qwen3-Coder-Next	`qwen/qwen3-coder-next`
Qwen3-Next-80B-A3B-Instruct	`qwen/qwen3-next-80b-a3b-instruct`
Qwen3 Next 80B A3B Thinking	`qwen/qwen3-next-80b-a3b-thinking`
Qwen3-32B	`qwen/qwen3-32b`
Qwen3-32B NIM for DGX Spark	`qwen/qwen3-32b-dgx-spark`
Qwen2.5 Coder 32B Instruct	`qwen/qwen2.5-coder-32b-instruct`
Qwen2.5 7B Instruct	`qwen/qwen-2.5-7b-instruct`
Riva Translate 4B Instruct	`nvidia/riva_translate_4b_instruct`
Riva-Translate-4b-Instruct-v1.1	`nvidia/riva-translate-4b-instruct-v1.1`
Sarvam - M	`sarvamai/sarvam-m`
SILMA 9B Instruct v1.0	`silma-ai/silma-9b-instruct-v1.0`
StarCoder2 7B	`bigcode/starcoder2-7b`
StarCoderBase 15.5B	`bigcode/starcoderbase-15b`
Stockmark-2-100B-Instruct	`stockmark/stockmark-2-100b-instruct`
Teuken 7B Instruct Commercial v0.4	`opengpt-x/teuken-7b-instruct-commercial-v0.4`