Llama 2 70B Chat HF — Hardware Requirements & GPU Compatibility
Chat
Llama 2 70B Chat HF is a 69.0B-parameter open language model from Meta in the Llama 2 family. At BF16 it needs about 151.75 GB of VRAM — see which GPUs and Macs can run it below.
Llama 2 70B Chat HF runs on a 192 GB+ Mac or a datacenter GPU
Best local pick → Mac Pro M2 Ultra (192 GB) · BF16 · 156.4 GB · ~4 tok/s
Macs from 192 GB (BF16 · ~4 tok/s)
Score 50.4#100 of 100 open#78 of 78 that fit ≤192 GBLlama 2 Community
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
50.4
- 90% range 31.8–69.0
- #100 of 100 open models
- #78 of 78 that fit ≤192 GB
Run Llama 2 70B Chat HF
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve meta-llama/Llama-2-70b-chat-hf --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 B200 (192 GB) (35 GB headroom) · BF16
- Generation speed
- ~34 tok/s
- generation speed
- Cost per 1M output tokens
- $48.43
- per 1M output tokens
How Much VRAM Does Llama 2 70B Chat HF Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 151.8 GB | 161.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on B200 192 GB |
Selected BF16
Brain floating point 16156.4GBat 16K context
- Weights
- 150.8 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Server-class: ~156.4 GB at 16K context · one B200 192 GB
Which Hardware Can Run Llama 2 70B Chat HF?
BF16 · 151.8 GBLlama 2 70B Chat HF (BF16) requires 151.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 198+ GB is recommended. No consumer GPU has enough memory. 4 devices with unified memory can run Llama 2 70B Chat HF, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (192 GB).
192+ GB
fits · 35.6 GB free · up to 32K ctx
6 GPUs · 1 Mac class · 2 device families
B200~34Simulate output on B200B300~34Simulate output on B300Instinct MI350X~32Simulate output on Instinct MI350XM3 Ultra256–512 GB~4Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~115Simulate output on NVIDIA DGX H1008x A100 SXM4~70Simulate 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 128 GB: too small for this model (needs ≥ 156.4 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is Llama 2 70B Chat HF?
The llmrun Score merges 4 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤192 GB tier
- Llama 2 70B Chat HF
- Other models
- Best Score for its VRAM
#78 of 78 that fit ≤192 GB
Each dot is a model at the best quant that fits ≤192 GB. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤192 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 9 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Jul 2023Independent
Independent26.3%
#88/98 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1170.7
#161/187 open
- AIME 2024/2025
Epoch AI · Jul 2023Independent
Independent0.0%
#86/86 open
- MATH Level 5
Epoch AI · Jul 2023Independent
Independent3.3%
#39/39 open
- MMLU
Epoch AI · Jul 2023Lab-reported
Lab-reported59.9%
#47/85 open
- DTBench
Epoch AI · Jul 2023Lab-reported
Lab-reported41.6%
#68/68 open
In the ≤192 GB class
78 models fit ≤192 GBSame Score, only models that fit ≤192 GB at their best quant: the ranking a ≤192 GB owner actually chooses from.
- #1
MiMo V2.6 Flash RLXiaomi
131.9
~229 tok/s · 186.5 GB
- #2
DeepSeek V4 Flash 0731DeepSeek
127.6
~181 tok/s · 183.0 GB
- #3
GLM 5.3 FlashZ.ai
124.2
~228 tok/s · 185.1 GB
- #4
Qwen3.8 27BAlibaba
122.1
~299 tok/s · 17.9 GB
- #5
Inkling SmallThinking Machines
119.6
~399 tok/s · 162.7 GB
- #6
Qwen3.5 397B A17BAlibaba
113.0
~185 tok/s · 182.1 GB
Alternatives to Llama 2 70B Chat HF
Llama 2 family · 10 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Best alternative at this size
MiMo V2.6 Flash RL
Xiaomi
- 81.5 higher Score
- 30.1 GB more VRAM
Score 131.9 · 186.5 GB
Other vendor
DeepSeek V4 Flash 0731
DeepSeek
- 77.2 higher Score
- 26.6 GB more VRAM
Score 127.6 · 183 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| GLM 5.3 FlashZ.ai | 124.2 | 205.2 GB | —needs > 24 GB | similar score |
| Inkling SmallThinking Machines | 119.6 | 162.7 GB | —needs > 24 GB | similar score |
| Qwen3.5 397B A17BAlibaba | 113.0 | 242.6 GB | —needs > 24 GB | similar score |
| MiniMax M3MiniMax | 112.5 | 258 GB | —needs > 24 GB | similar score |
| MiniMax M2.5MiniMax | 111.8 | 139.6 GB | —needs > 24 GB | similar score |
| DeepSeek V4 FlashDeepSeek | 109.3 | 175.1 GB | —needs > 24 GB | similar score |
| Mixtral 8x7B Instruct v0.1Mistral AI | 65.1 | 23.5 GB | ~92 | similar score |
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | similar score |
| Llama 3.1 8B InstructMeta | 62.5 | 7.9 GB | ~124 | similar score |
| MiniMax M2.1MiniMax | 106.2 | 139.6 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Meta
- Family
- Llama 2
- Parameters
- 69.0B
- Release Date
- 2023-07-14
- License
- Llama 2 Community
Frequently Asked Questions
How much VRAM does Llama 2 70B Chat HF need?
Llama 2 70B Chat HF requires 151.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 69.0B × 16 bits ÷ 8 = 138 GB
KV Cache + Overhead ≈ 13.8 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 2 70B Chat HF?
No — Llama 2 70B Chat HF requires at least 151.8 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run Llama 2 70B Chat HF on a Mac?
Yes — Mac Pro M2 Ultra (192 GB) and 3 other Macs can run Llama 2 70B Chat HF. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 151.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 2 70B Chat HF locally?
Yes — Llama 2 70B Chat HF can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 2 70B Chat HF?
At BF16, Llama 2 70B Chat HF can reach ~32 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 ÷ 151.8 × 0.65 = ~34 tok/s
Estimated speed at BF16 (151.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 2 70B Chat HF?
At BF16, the download is about 137.95 GB.
Which GPUs can run Llama 2 70B Chat HF?
No single consumer GPU has enough VRAM to run Llama 2 70B Chat HF at BF16 (151.8 GB). Multi-GPU or professional hardware is required.
Which devices can run Llama 2 70B Chat HF?
4 devices with unified memory can run Llama 2 70B Chat HF at BF16 (151.8 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.