RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Llama 2 7B Chat needs ~7.1 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
71.3 tok/s
TTFT
2714 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 71.3 tok/s | 1480 ms | 34K |
| Coding | C | Tight fit | 71.3 tok/s | 2714 ms | 34K |
| Agentic Coding | C | Runs with offload | 71.3 tok/s | 3947 ms | 34K |
| Reasoning | C | Tight fit | 71.3 tok/s | 3207 ms | 34K |
| RAG | C | Runs with offload | 71.3 tok/s | 4934 ms | 34K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 2 7B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 2 7B Chat (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_S | 3 | 3.4 GB | Low | C54 |
NVFP4 | 4 | 3.9 GB | Medium | C54 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run Llama 2 7B Chat on your machine.
Run
lms load hf-thebloke--llama-2-7b-chat-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeRaises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 3060 Ti 8GB can run Llama 2 7B Chat with a C grade (Tight fit). Expected decode speed: 71.3 tok/s.
Llama 2 7B Chat (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 2 7B Chat is Q4_K_M, which balances quality and memory efficiency.
On RTX 3060 Ti 8GB, Llama 2 7B Chat achieves approximately 71.3 tokens per second decode speed with a time-to-first-token of 2714ms using Q4_K_M quantization.
For coding workloads, Llama 2 7B Chat on RTX 3060 Ti 8GB receives a C grade with 71.3 tok/s and 34K context.
On RTX 3060 Ti 8GB, Llama 2 7B Chat can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
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