Written by Jakub Rusinowski · Last updated January 20, 2025
These figures are for DeepSeek-R1-Distill-Llama-8B, a distill of Llama-3.1-8B — not the full DeepSeek R1. The full DeepSeek R1 (671B) needs about 405 GB of weights at Q4_K_M and is a different model.
Yes, but it is tight
Yes, but it is tight — DeepSeek R1 Distill Llama 8B at Q5_K_M needs about 7.5 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.5 GB before the runtime starts swapping. Expect ~30.6 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~30.6 tok/s
| Usable memory for models | 8 GB |
| Memory bandwidth | 272 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $1,099 (lib/data/laptops.ts (street price), checked 2026-07-06) |
| Quant | Memory needed | Fits 8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 17.9 GB | ✗ No | — | — | 16 GB |
| Q8_0 | 10.4 GB | ✗ No | — | — | 8.5 GB |
| Q6_K | 8.4 GB | ✗ No | — | — | 6.6 GB |
| Q5_K_M | 7.5 GB | ✓ Yes | 8K | ~30.6 tok/s | 5.7 GB |
| Q4_K_M | 6.7 GB | ✓ Yes | 16K | ~35 tok/s | 4.8 GB |
| Q3_K_M | 5.3 GB | ✓ Yes | 16K | ~46.3 tok/s | 3.4 GB |
| Q2_K | 4.5 GB | ✓ Yes | 32K | ~56.3 tok/s | 2.6 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| DeepSeek R1 (671B) | 406.5 GB | ✗ Too large | — |
| DeepSeek R1 Distill Qwen 32B | 22.3 GB | ✗ Too large | — |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | ✗ Too large | — |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~35 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, but it is tight — DeepSeek R1 Distill Llama 8B at Q5_K_M needs about 7.5 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.5 GB before the runtime starts swapping. Expect ~30.6 tok/s (estimated), with room for about 8,192 tokens of context.
Q5_K_M — it needs about 7.5 GB of the 8 GB available, downloads as roughly 5.7 GB, and runs at an estimated 30.6 tokens/sec with up to 8K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
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