Meta · Released July 2023
Llama 2 7B Chat
What it takes to run Llama 2 7B Chat locally: memory by quantization, the GPUs that handle it, and how fast it runs.
Updated
- Parameters
- 6.7B
- Architecture
- Dense
- Context
- 4K tokens
- License
- Llama 2 Community License
- Memory at Q4_K_M
- ~6.9GB
What do you need to run Llama 2 7B Chat?
Llama 2 7B Chat needs about 6.9GB of VRAM at Q4_K_M with 8K context (4.1GB for the weights plus KV cache and overhead). The smallest GPU that runs it comfortably is a 8GB card like the RTX 5060.
What to buy
GPUs for Llama 2 7B Chat
Speeds are estimates at the quantization shown with 8K context. We may earn a commission when you buy through these links. Learn more.
Memory
Llama 2 7B Chat VRAM requirements by quantization
Lower-bit quantizations shrink the model at some cost to quality. Q4_K_M is the usual sweet spot. Totals include the KV cache for the context length shown plus runtime overhead.
| Quantization | Quality | Weights | Total @ 4K ctx |
|---|---|---|---|
| Q2_K | Rough | 2.5 GB | 5.4 GB |
| Q3_K_M | Usable | 3.3 GB | 6.2 GB |
| Q4_K_M | RecommendedDefault | 4.1 GB | 6.9 GB |
| Q5_K_M | Very good | 4.8 GB | 7.7 GB |
| Q6_K | Near-lossless | 5.5 GB | 8.4 GB |
| Q8_0 | Practically lossless | 7.2 GB | 10 GB |
| BF16 | Full precision | 13 GB | 17 GB |
KV cache note: Full multi-head attention with no grouped-query sharing: 2 (K and V) x 32 layers x 32 KV heads x 128 dims x 2 bytes = 524,288 bytes (512 KiB) per token, so the full 4K context costs about 2.1GB.
Performance
How fast Llama 2 7B Chat runs
Single-user generation speed on popular GPUs that fit the model entirely in memory, at the best quantization each can hold.
RTX 5090
Q8_0 · 32GB
~165tok/s
RTX PRO 6000
Q8_0 · 96GB
~165tok/s
RTX PRO 5000 72GB
Q8_0 · 72GB
~130tok/s
M5 Ultra 256GB
Q8_0 · 256GB
120tok/s
RTX 4090
Q8_0 · 24GB
~100tok/s
RTX 5080
Q8_0 · 16GB
~97tok/s
RTX 6000 Ada
Q8_0 · 48GB
~97tok/s
RTX 5070 Ti
Q8_0 · 16GB
~91tok/s
RTX 3090
Q8_0 · 24GB
~86tok/s
M5 Ultra 512GB
Q8_0 · 512GB
~83tok/s
Measured
Llama 2 7B Chat benchmarks
- RTX 5090Q4_0 · llama.cpp290 tok/s llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA
- RTX PRO 6000Q4_0 · llama.cpp275 tok/s llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA
- RTX 5090Q4_0 · llama.cpp265 tok/s llama.cpp discussion #10879: Performance of llama.cpp with Vulkan
- RTX 4090Q4_0 · llama.cpp190 tok/s llama.cpp discussion #10879: Performance of llama.cpp with Vulkan
- RTX 4090Q4_0 · llama.cpp185 tok/s llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA
- RTX 5080Q4_0 · llama.cpp185 tok/s llama.cpp discussion #10879: Performance of llama.cpp with Vulkan
- RX 7900 XTXQ4_0 · llama.cpp185 tok/s llama.cpp discussion #10879: Performance of llama.cpp with Vulkan
- RTX 5080Q4_0 · llama.cpp180 tok/s llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA
- M5 Ultra 256GBQ4_0 · llama.cpp180 tok/s llama.cpp discussion #4167: Performance of llama.cpp on Apple Silicon M-series
- RTX 5070 TiQ4_0 · llama.cpp175 tok/s llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA
Overview
About Llama 2 7B Chat
Llama 2 7B was Meta's July 2023 entry-level open model and the default local model of its era. Today it is useful mainly as a historical baseline: current 4B to 9B models beat it by a wide margin on reasoning, coding, and instruction following. It runs on almost anything, needing about 4GB at Q4_K_M plus up to 2GB of KV cache at its 4K context limit, so any 8GB GPU or 8GB Apple silicon Mac handles it. For new projects, pick Qwen3.5 9B or Gemma 4 E4B instead.
Strengths
- +Tiny footprint: about 4GB at Q4_K_M, fast even on old 6GB and 8GB GPUs
- +Supported by every runtime, fine-tuning tool, and tutorial
- +Large archive of community fine-tunes for niche tasks
Weaknesses
- −4K context window is too short for documents, codebases, or long chats
- −Far behind 2025 and 2026 small models on reasoning, coding, and factual accuracy
- −No grouped-query attention, so its KV cache per token is four times larger than Llama 3.1 8B's
- −Llama 2 license adds use restrictions and a 700M monthly user cap
Setup
How to run Llama 2 7B Chat
$ ollama run llama2:7b$ llama-server -hf TheBloke/Llama-2-7B-Chat-GGUF:Q4_K_M -c 8192 -ngl 99Weights: meta-llama/Llama-2-7b-chat-hf · GGUF: TheBloke/Llama-2-7B-Chat-GGUF
Alternatives
Models like Llama 2 7B Chat
Frequently asked questions
How much VRAM do you need to run Llama 2 7B Chat?
About 6.9GB at Q4_K_M with 8K tokens of context: 4.1GB of weights, 2.1GB of KV cache, and 0.7GB of runtime overhead. Higher-precision quants and longer contexts need more; see the table above.
What is the cheapest GPU that can run Llama 2 7B Chat?
The smallest card in our database that runs it comfortably is the RTX 5060 (8GB), at Q4_K_M and ~81 tokens/sec.
Is Llama 2 7B Chat free for commercial use?
Llama 2 7B Chat is released under the Llama 2 Community License. Commercial use is allowed with some conditions; read the license before shipping a product.
How long a context does Llama 2 7B Chat support?
Up to 4,096 tokens (4K). Long contexts need a lot of extra memory for the KV cache, so most local setups run 8K to 32K.
Sources: Llama 2 7B Chat model card (Hugging Face), TheBloke Llama 2 7B Chat GGUF files. Capability score basis: Llama Builds scale anchor (October 2026) for 2023-era 7B chat models; not scored on current Artificial Analysis Intelligence Index versions.