How much VRAM does DeepSeek-Coder-V2-Lite Instruct need?

About 10.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 11.0 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-Coder-V2-Lite Instruct run on a 12GB GPU?

Yes — at Q4_K_M and 4K context it needs about 10.1 GB, which leaves 1.9 GB spare on a RTX 3060 12G, or on any of the other 12 GB cards here (10 in all). That is the smallest size in this index that clears it comfortably; 65 of 78 do.

Which quantization of DeepSeek-Coder-V2-Lite Instruct should I use?

Q8_0 has the lowest published quality loss (0.2%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

DeepSeek-Coder-V2-Lite Instruct

16B-A2.4B

DeepSeek

MoE architecture coding model. Active params ~2.4B, total ~16B. Exceptional code quality. 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.

⬇ 140.3K HF downloads♥ 198 likesbartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF· stats from 10/20/2026

Consumer GPUMac / Apple Silicon

164K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

99.8%

Accuracy Retained

Calculate VRAM Hugging Face Compare

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	11.1 GB	3.1%	145 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	17.5 GB	0.2%	118 tok/s	Estimated	Calc HF
AWQ	INT4	4	9.8 GB	4.1%	192 tok/s	Estimated	Calc HF

Similar models

Compare with DeepSeek-V2-Lite Chat

16B-A2.4B

DeepSeek-V2-Lite Chat

DeepSeek

Consumer GPUMac / Apple Silicon

9.6 GBmin VRAM97.0%accuracy

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.

14B

DeepSeek-R1-Distill-Qwen-14B

DeepSeek

Consumer GPU

9.2 GBmin VRAM98.0%accuracy

R1 reasoning distilled into 14B. Huge community interest; excellent chain-of-thought.

DeepSeek-R1-Distill-Qwen-7B

DeepSeek

Consumer GPUMac / Apple Silicon

5.2 GBmin VRAM97.8%accuracy

R1 reasoning in a 7B footprint. Best value for 8–12GB VRAM CoT experiments.

DeepSeek-R1-Distill-Llama-8B

DeepSeek

Consumer GPUMac / Apple Silicon

4.9 GBmin VRAM98.8%accuracy

R1 reasoning distilled into Llama 3.1 8B. Best chain-of-thought for 8–12GB cards; huge community GGUF support.

Running DeepSeek-Coder-V2-Lite Instruct locally

At Q4_K_M and 4K of context, DeepSeek-Coder-V2-Lite Instruct needs about 10.1 GB — 9.0 GB of weights, 0.12 GB of KV cache and a 0.9 GB activation buffer. The smallest cards in this index that clear that comfortably are 12 GB ones — 10 of them, from the RTX 3060 12G up — and 65 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 0.83 GB, taking the total to 11.0 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 160K; holding all of it at Q4_K_M would need about 15 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q8_0 carries the lowest published perplexity loss at 0.2%. The fastest level listed is AWQ INT4 at about 192 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-Coder-V2-Lite Instruct need?: About 10.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 11.0 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-Coder-V2-Lite Instruct run on a 12GB GPU?: Yes — at Q4_K_M and 4K context it needs about 10.1 GB, which leaves 1.9 GB spare on a RTX 3060 12G, or on any of the other 12 GB cards here (10 in all). That is the smallest size in this index that clears it comfortably; 65 of 78 do.
Which quantization of DeepSeek-Coder-V2-Lite Instruct should I use?: Q8_0 has the lowest published quality loss (0.2%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

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 3060 12G12GB · +1.9 Radeon RX 6700 XT12GB · +1.9 Radeon RX 7700 XT12GB · +1.9 Arc B580 12G12GB · +1.9 RTX 4070 Ti12GB · +1.9
Tight but possible: Arc B570 10G10GB RTX 3080 10G10GB
Just misses: RTX 4060+2.1GB over RTX 4060 Ti 8G+2.1GB over
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
Try it yourself: Size it in the calculator Generate 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 2026-10-20 RSS → /feed.xml