DeepSeek-R1-Distill-Llama 8B VRAM: what it needs at Q4_K_M, Q8_0 and FP16
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KV cache is counted at FP16. Overhead is a fixed 1.5 GiB estimate for the runtime and compute buffers. A custom size borrows the architecture of the nearest preset.
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GPU tiers
- 8 GiBfitsRTX 4060, RTX 3070, RX 7600
- 12 GiBfitsRTX 4070, RTX 3060 12GB, RTX 5070
- 16 GiBfitsRTX 4060 Ti 16GB, RTX 4080, RTX 5080
- 24 GiBfitsRTX 4090, RTX 3090, RX 7900 XTX
- 32 GiBfitsRTX 5090, V100 32GB
- 48 GiBfitsRTX A6000, RTX 6000 Ada, L40S
- 80 GiBfitsA100 80GB, H100 80GB
At Q4_K_M with an 8,192-token context, DeepSeek-R1-Distill-Llama 8B needs about 7.05 GiB of VRAM: 4.55 GiB of weights, 1.00 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 8 GiB (RTX 4060, RTX 3070, RX 7600).
How much VRAM DeepSeek-R1-Distill-Llama 8B needs
DeepSeek-R1-Distill-Llama 8B, from January 2025, is Llama 3.1 8B fine-tuned to reason step by step. The R1 distills are not DeepSeek-R1 itself, which is a 671B mixture-of-experts model; each one is an existing Qwen2.5 or Llama base fine-tuned on reasoning traces generated by R1, so its memory footprint is exactly that of its base architecture.
With 8,192 tokens of context the estimate is 17.4 GiB at FP16, 10.4 GiB at Q8_0 and 7.05 GiB at Q4_K_M. FP16 needs a 24 GiB card, Q8_0 needs a 12 GiB card, and Q4_K_M needs an 8 GiB card. The weights are 8 billion parameters times the bits per weight of each format (16, 8.5 and 4.89), divided by eight; everything is in GiB, the unit GPU memory is sold in.
Context length and the KV cache
The KV cache stores a key and a value vector for every token in every layer. DeepSeek-R1-Distill-Llama 8B has 32 layers, 8 KV heads and a head dimension of 128, read from its Hugging Face config.json, so at FP16 each token costs 128 KiB. That is 1.00 GiB at 8,192 tokens, and 16.0 GiB at the maximum of 131,072, where the Q4_K_M total becomes 22.1 GiB and needs a 24 GiB card. The cache does not shrink with weight quantization; only a runtime option that quantizes the cache itself does.
Which quantization to pick
Q4_K_M at 7.05 GiB fits an 8 GiB card at 8,192 tokens; Q8_0 at 10.4 GiB needs a 12 GiB card.
Pitfalls
Reasoning models write a long chain of thought before the answer, often thousands of tokens, so budget context generously: a reply that runs out of KV cache stops mid-thought. This distill has twice the KV heads of the Qwen 7B distill, 128 KiB per token, so 16,384 tokens at Q4_K_M already need 8.05 GiB and overflow 8 GiB.
Llama 3.1 8B is the base architecture; DeepSeek-R1-Distill-Qwen 7B is the lighter-cache alternative.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can DeepSeek-R1 Llama 8B run on an 8 GB GPU?
At Q4_K_M with 8,192 tokens, yes: 7.05 GiB. For longer reasoning, 16,384 tokens need 8.05 GiB, so a 12 GiB card.
Which is lighter, DeepSeek-R1 Llama 8B or Qwen 7B?
The Qwen 7B distill. At 8,192 tokens and Q4_K_M this one needs 7.05 GiB; the Qwen 7B distill has half the KV heads and smaller weights.
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Estimates only; the KV cache is sized at FP16 and the overhead is a fixed allowance, so always confirm against your own runtime’s report before buying hardware. OpsCanopy is free and open.