Llama 3.3 Nemotron 70B Reward — Hardware Requirements & GPU Compatibility
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Llama 3.3 Nemotron 70B Reward is a 70.6B-parameter open language model from NVIDIA in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 43.30 GB of VRAM — see which GPUs and Macs can run it below.
Llama 3.3 Nemotron 70B Reward runs on a 48 GB+ Mac or a workstation GPU
Best local pick → Mac Studio M5 Max (48 GB) · Q4_K_M · 48 GB · ~10 tok/s
Macs from 48 GB (Q4_K_M · ~10 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.3 Nemotron 70B Reward
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve nvidia/Llama-3.3-Nemotron-70B-Reward --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on A100 80GB (32 GB headroom) · Q4_K_M
- Generation speed
- ~31 tok/s
- generation speed
- Cost per 1M output tokens
- $10.31
- per 1M output tokens
How Much VRAM Does Llama 3.3 Nemotron 70B Reward Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 31.0 GB | 41 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 35.7 GB | |
| 35.4 GB | 45.4 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 40.1 GB | |
| 43.3 GB | 53.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 48 GB | |
| 51.2 GB | 61.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 55.9 GB | |
| 59.2 GB | 69.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 63.9 GB | |
| 71.5 GB | 81.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 76.2 GB | |
| 142.1 GB | 152.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 146.8 GB |
Selected Q4_K_M
4-bit medium quantization48.0GBat 16K context
- Weights
- 42.3 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 24.0 GB more
Or .
Which Hardware Can Run Llama 3.3 Nemotron 70B Reward?
Q4_K_M · 43.3 GBLlama 3.3 Nemotron 70B Reward (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 85.6 GB. No consumer GPU has enough memory. 67 devices with unified memory can run Llama 3.3 Nemotron 70B Reward, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
48 GB
48 GB tier →fits · 0.0 GB free · up to 16K ctx
2 Mac classes · 1 device family
M5 Max48 GB~10Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~5Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~3Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 3.3 Nemotron 70B Reward
Llama 3 family · 65 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen2.5 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen2 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen1.5 72B ChatAlibaba | — | 86.6 GB | —needs > 24 GB | same size |
| CodeLlama 70B Instruct HFMeta | — | 23.2 GB | ~29 | same size |
| StableBeluga2Stability AI | — | 23.5 GB | ~29 | same size |
| Apertus 70B 2509swiss-ai | — | 48 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-05-28
- License
- Other
Frequently Asked Questions
How much VRAM does Llama 3.3 Nemotron 70B Reward need?
Llama 3.3 Nemotron 70B Reward requires 43.3 GB of VRAM at Q4_K_M, or 142.1 GB at BF16. Full 131K context adds up to 42.3 GB (85.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 1 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.
KV Cache + Overhead ≈ 43.3 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Llama 3.3 Nemotron 70B Reward?
Yes, at Q2_K (31.0 GB) or lower. Higher quantizations like Q3_K_M (35.4 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
What's the best quantization for Llama 3.3 Nemotron 70B Reward?
For Llama 3.3 Nemotron 70B Reward, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (51.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 31.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.3 Nemotron 70B Reward on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Llama 3.3 Nemotron 70B Reward. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 43.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 3.3 Nemotron 70B Reward locally?
Yes — Llama 3.3 Nemotron 70B Reward can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.3 Nemotron 70B Reward?
At Q4_K_M, Llama 3.3 Nemotron 70B Reward can reach ~111 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 ÷ 43.3 × 0.65 = ~120 tok/s
Estimated speed at Q4_K_M (43.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3.3 Nemotron 70B Reward?
At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (Q2_K) is 29.99 GB.
Which GPUs can run Llama 3.3 Nemotron 70B Reward?
No single consumer GPU has enough VRAM to run Llama 3.3 Nemotron 70B Reward at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.
Which devices can run Llama 3.3 Nemotron 70B Reward?
67 devices with unified memory can run Llama 3.3 Nemotron 70B Reward at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.