Llama 3 1 Nemotron 51B Instruct — Hardware Requirements & GPU Compatibility
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Llama 3 1 Nemotron 51B Instruct is a 51B-parameter open language model from NVIDIA in the Llama 3 family. It supports a context window of up to 131,072 tokens. At BF16 it needs about 112.20 GB of VRAM — see which GPUs and Macs can run it below.
Llama 3 1 Nemotron 51B Instruct runs on a 128 GB+ Mac or a datacenter GPU
Best local pick → Mac Pro M2 Ultra (128 GB) · BF16 · 116.5 GB · ~5 tok/s
Macs from 128 GB (BF16 · ~5 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3 1 Nemotron 51B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve nvidia/Llama-3_1-Nemotron-51B-Instruct --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on H200 (141 GB) (24 GB headroom) · BF16
- Generation speed
- ~28 tok/s
- generation speed
- Cost per 1M output tokens
- $35.87
- per 1M output tokens
How Much VRAM Does Llama 3 1 Nemotron 51B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 112.2 GB | 121.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on Instinct MI250X 128 GB |
Selected BF16
Brain floating point 16116.5GBat 16K context
- Weights
- 111.3 GB
- KV cache
- 4.9 GB
- Overhead
- 0.3 GB
Server-class: ~116.5 GB at 16K context · one Instinct MI250X 128 GB
Which Hardware Can Run Llama 3 1 Nemotron 51B Instruct?
BF16 · 112.2 GBLlama 3 1 Nemotron 51B Instruct (BF16) requires 112.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 146+ GB is recommended. No consumer GPU has enough memory. 17 devices with unified memory can run Llama 3 1 Nemotron 51B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (128 GB).
128–144 GB
fits · 11.5 GB free · up to 32K ctx
4 GPUs · 2 Mac classes · 1 device family
GH200 Grace Hopper Superchip~28Simulate output on GH200 Grace Hopper SuperchipH200 NVL~28Simulate output on H200 NVLH200 SXM~28Simulate output on H200 SXMM2 Ultra*128 GB~5Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~4Simulate output on Mac Studio M5 Max (128 GB)GB10~2Simulate output on ASUS Ascent GX10192+ GB
fits · 75.5 GB free · up to 128K ctx
6 GPUs · 1 Mac class · 2 device families
B200~46Simulate output on B200B300~46Simulate output on B300Instinct MI350X~43Simulate output on Instinct MI350XM3 Ultra*256–512 GB~5Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~155Simulate output on NVIDIA DGX H1008x A100 SXM4~95Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at BF16
Consumer GPUs (up to 32 GB) and Macs up to 96 GB: too small for this model (needs ≥ 116.5 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is Llama 3 1 Nemotron 51B Instruct?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
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Alternatives to Llama 3 1 Nemotron 51B Instruct
Llama 3 family · 64 models →Similar models
2| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Kimi Linear 48B A3B BaseMoonshot AI | — | 23.4 GB | ~275 | same size |
| AI21 Jamba Mini 1.5AI21 Labs | — | 117.8 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Llama 3
- Parameters
- 51B
- Architecture
- DeciLMForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-09-22
- License
- Other
Frequently Asked Questions
How much VRAM does Llama 3 1 Nemotron 51B Instruct need?
Llama 3 1 Nemotron 51B Instruct requires 112.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 51B × 16 bits ÷ 8 = 102 GB
KV Cache + Overhead ≈ 10.2 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Llama 3 1 Nemotron 51B Instruct?
No — Llama 3 1 Nemotron 51B Instruct requires at least 112.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run Llama 3 1 Nemotron 51B Instruct on a Mac?
Yes — Mac Pro M2 Ultra (128 GB) and 14 other Macs can run Llama 3 1 Nemotron 51B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 112.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 3 1 Nemotron 51B Instruct locally?
Yes — Llama 3 1 Nemotron 51B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3 1 Nemotron 51B Instruct?
At BF16, Llama 3 1 Nemotron 51B Instruct can reach ~43 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 112.2 × 0.65 = ~46 tok/s
Estimated speed at BF16 (112.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3 1 Nemotron 51B Instruct?
At BF16, the download is about 102.00 GB.
Which GPUs can run Llama 3 1 Nemotron 51B Instruct?
No single consumer GPU has enough VRAM to run Llama 3 1 Nemotron 51B Instruct at BF16 (112.2 GB). Multi-GPU or professional hardware is required.
Which devices can run Llama 3 1 Nemotron 51B Instruct?
17 devices with unified memory can run Llama 3 1 Nemotron 51B Instruct at BF16 (112.2 GB), including ASUS Ascent GX10, Mac Pro M2 Ultra (128 GB), Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.