Llama 2 7B Chat HF — Hardware Requirements & GPU Compatibility
Chat
Meta Llama 2 7B Chat is a 7-billion parameter instruction-tuned model from Meta's Llama 2 family, optimized for dialogue use cases. It was fine-tuned using supervised fine-tuning and RLHF on top of the Llama 2 7B base model, with a 4K token context window. This model is suitable for basic conversational AI tasks and runs efficiently on consumer GPUs. While newer Llama generations offer improved performance, Llama 2 7B Chat remains a well-understood and widely-supported option for local inference. Released under the Llama 2 Community License.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~147 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~16 tok/s)
Llama 2 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 2 7B Chat HF
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
ollama run llama2:7b-chat-q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.1 GB free · 16K context
Download: llama-2-7b-chat.Q4_K_M.gguf4.08 GBTheBloke
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~133 tok/s
- generation speed
- Cost per 1M output tokens
- $0.25
- per 1M output tokens
How Much VRAM Does Llama 2 7B Chat HF Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.1 GB | 8.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~208 tok/s | |
| 3.2 GB | 8.5 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~202 tok/s | |
| 3.6 GB | 8.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~182 tok/s | |
| 3.7 GB | 9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~177 tok/s | |
| 4.5 GB | 9.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~147 tok/s | |
| 5.3 GB | 10.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~124 tok/s | |
| 6.1 GB | 11.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~107 tok/s | |
| 7.4 GB | 12.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~88 tok/s |
Selected Q4_K_M
4-bit medium quantization6.9GBat 16K context
- Weights
- 3.8 GB
- KV cache
- 2.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.1 GB free · ~147 tok/s
≥50 tok/s needs ≥342 GB/s · RTX 5090 ~262, RTX 3090 Ti ~147
Which Hardware Can Run Llama 2 7B Chat HF?
Q4_K_M · 4.5 GBLlama 2 7B Chat HF (Q4_K_M) requires 4.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Llama 2 7B Chat HF, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.1 GB free · up to 16K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~111Simulate output on RTX 3080RTX 2080 Ti~90Simulate output on RTX 2080 TiRTX 3070 Ti~89Simulate output on RTX 3070 TiM3*8 GB~16Simulate output on iMac 24" M3 (8 GB)Orin Nano~15Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~11Simulate output on iPhone 1720 GB
fits · 13.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~108Simulate output on RX 7900 XT32–40 GB
fits · 25.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~262Simulate output on RTX 5090M5 Max36 GB~72Simulate output on Mac Studio M5 Max (36 GB)M532 GB~24Simulate output on MacBook Air 13" M5 (32 GB)Orin~30Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~20Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 2 7B Chat HF?
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 2 7B Chat HF
Llama 2 family · 11 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
| Llava 1.5 7B HFllava-hf | — | 5 GB | ~141 | same size |
| Chatglm3 6BZ.ai | — | 5.2 GB | ~159 | same size |
Fine-tunes & variants(2)
Specifications
- Publisher
- Meta
- Family
- Llama 2
- Parameters
- 6.7B
- Release Date
- 2023-07-13
- License
- Llama 2 Community
Frequently Asked Questions
How much VRAM does Llama 2 7B Chat HF need?
Llama 2 7B Chat HF requires 4.5 GB of VRAM at Q4_K_M, or 14.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 4.8 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 0.5 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
What's the best quantization for Llama 2 7B Chat HF?
For Llama 2 7B Chat HF, Q4_K_M (4.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 2 7B Chat HF on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Llama 2 7B Chat HF. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 2 7B Chat HF locally?
Yes — Llama 2 7B Chat HF can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 2 7B Chat HF?
At Q4_K_M, Llama 2 7B Chat HF can reach ~1079 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~147 tok/s. 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 ÷ 4.45 × 0.65 = ~1169 tok/s
Estimated speed at Q4_K_M (4.5 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 7B Chat HF?
At Q4_K_M, the download is about 4.04 GB. The full-precision BF16 version is 13.48 GB. The smallest option (Q2_K) is 2.86 GB.
Which GPUs can run Llama 2 7B Chat HF?
52 consumer GPUs can run Llama 2 7B Chat HF at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llama 2 7B Chat HF?
163 devices with unified memory can run Llama 2 7B Chat HF at Q4_K_M (4.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.