DeepSeek-R1-Distill-Llama-8B
8BDeepSeek
R1 reasoning distilled into Llama 3.1 8B. Best chain-of-thought for 8–12GB cards; huge community GGUF support.
131K
Max Context
4
Quant Variants
GGUF Q5_K_M
Best Quality
+1.2%
Perplexity rise (best build)
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
| Format | Level | BPW | VRAM | PPL Loss | Speed | Source | Actions |
|---|---|---|---|---|---|---|---|
| GGUF | Q4_K_M | 4.85 | 5.6 GB | 2.6% | 145 tok/s | Community | |
| GGUF | Q5_K_M | 5.68 | 6.5 GB | 1.2% | 128 tok/s | Estimated | |
| EXL2 | 4.65bpw | 4.65 | 5.4 GB | 1.9% | 210 tok/s | Estimated | |
| AWQ | INT4 | 4 | 4.8 GB | 3.5% | 188 tok/s | Estimated |
Which file each size is for
A file size is not a memory figure: the VRAM column is the calculator's total at 4K context — weights plus KV cache plus a 10% activation buffer. Units differ on purpose: file sizes are decimal GB, as Hugging Face lists them; the VRAM column is GiB (1024³ bytes) with 2% added to the weights, so 7.12 GB of file counts as about 6.8 GiB there. A vision projector (mmproj), where the repo ships one, is a separate download and is in neither number.
- GGUF Q4_K_MSized at the calculator's generic rate for this level; no file was checked.
- GGUF Q5_K_MSized at the calculator's generic rate for this level; no file was checked.
- EXL2 4.65bpwSized at the calculator's generic rate for this level; no file was checked.
- AWQ INT4Sized at the calculator's generic rate for this level; no file was checked.
Similar models
Compare with DeepSeek-R1-Distill-Qwen-7BDeepSeek-R1-Distill-Qwen-7B
DeepSeek
R1 reasoning in a 7B footprint. Best value for 8–12GB VRAM CoT experiments.
DeepSeek-V2-Lite Chat
DeepSeek
MoE general model (~2.4B active). Long context and strong multilingual chat. Its attention is MLA: a GGUF converted by current llama.cpp (tensors attn_k_b / attn_v_b) caches about 1 GB at 32K, which is what the estimates here assume. An older conversion with a single attn_kv_b tensor caches full keys and values instead — about 8.4 GB at 32K.
DeepSeek-R1-Distill-Qwen-14B
DeepSeek
R1 reasoning distilled into 14B. Huge community interest; excellent chain-of-thought.
DeepSeek-R1-Distill-Qwen-32B
DeepSeek
R1 distilled to 32B. Near-frontier reasoning on a single 24GB card (Q3/Q4).
Running DeepSeek-R1-Distill-Llama-8B locally
At Q4_K_M and 4K of context, DeepSeek-R1-Distill-Llama-8B needs about 5.6 GB — 4.6 GB of weights, 0.50 GB of KV cache and a 0.5 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 9 of them, from the RTX 4060 up — and 76 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 3.50 GB, taking the total to 9.5 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 23 GB.
Which build to download
This index tracks 3 formats for it — GGUF, EXL2, AWQ — across 4 levels. Q5_K_M carries the lowest published perplexity loss at 1.2%. The fastest level listed is EXL2 4.65bpw at about 210 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does DeepSeek-R1-Distill-Llama-8B need?
- About 5.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 9.5 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will DeepSeek-R1-Distill-Llama-8B run on a 8GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 5.6 GB, which leaves 2.4 GB spare on a RTX 4060, or on any of the other 8 GB cards here (9 in all). That is the smallest size in this index that clears it comfortably; 76 of 78 do.
- Which quantization of DeepSeek-R1-Distill-Llama-8B should I use?
- Q5_K_M has the lowest published quality loss (1.2%), and Q4_K_M is the level most people run. All 4 levels in the index are Q4_K_M, Q5_K_M, EXL2 4.65bpw, AWQ INT4.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 40608GB · +2.4RTX 4060 Ti 8G8GB · +2.4Radeon RX 9060 XT 8G8GB · +2.4RTX 5060 Ti 8G8GB · +2.4RTX 50608GB · +2.4
- Tight but possible
- Mac M1 8G8GBMac M3 8G8GB
- Compared here
- GGUF vs AWQGGUF vs EXL2AWQ vs EXL2
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-29 RSS → /feed.xml