Models › DeepSeek-V2 family

DeepSeek-Coder-V2-Instruct

deepseek-ai DeepSeek License (deepseek-license) out 2 years ago

236B parameters (6 of 160 experts per token), text in, text out, 160K tokens of context.

Verified 2026-10-25 Rank #495 in LLMs · score 76.7 method v1 Sources 3 Downloads 30d 4.7k · #547 of 619 tracked

Can I run it?141GB at INT4Estimatefits 2×80 GBmethod
Can I use it?DeepSeek License (deepseek-license)Attribution requiredlicence file2026-10-24
Do people use it?4.7kdownloads, 30 days#547 of 619 we track · below the median+2 likes, downloads −24% this week717 likesHugging Face2026-10-24

Worth reading and watching

Picked by rule from 8 tracked pieces: the lab's own post, the most shared, a field report, the latest.

8 best of 8 tracked

The 2 picks above, plus these 6. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleDeepSeek V2, near GPT4 performance with 30% GPT3.5 cost ($0.14M input tokens)deepseek.com · 2024-05-08 · 16 HN pts · official
  2. CodeDeepSeek-V2: A Strong, Economical, and Efficient Moe Language Modelgithub.com · 2024-05-07 · 14 HN pts
  3. PaperDeepSeek-V2: A Strong, Economical, and Efficient MOE Language Modelarxiv.org · 2025-01-01 · 3 HN pts
  4. CodeDeepSeek-V2 integrated, RAGFlow v0.5.0 is releasedgithub.com · 2024-05-08 · 7 HN pts
  5. DiscussionLocal DeepSeeK-V2 Inference: 14T/S for Decode W Only 21GB 4090 and 136GB DRAMold.reddit.com · 2024-07-30 · 8 HN pts
  6. ArticleBest Open Source LLM:Tencent Hunyuan-Large Outperforming Llama 3 and DeepSeek V2techwavearena.com · 2024-11-08 · 1 HN pts

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 12 quantized builds and 79 Spaces.

  • As a service0no API provider listed
  • On own hardware12quantized builds
  • Built on11 fine-tunes · 0 adapters · 0 merges
  • In apps79Hugging Face Spaces

Top Spaces running it: KBaba7/Quant ♥ 51 · FallnAI/Quantize-HF-Models ♥ 19 · bhaskartripathi/LLM_Quantization ♥ 16 · openfree/LLM_Quantization ♥ 16

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits two 80 GB GPUs at INT4; too big for one

About 141 GB at INT4, 283 GB at FP8, 566 GB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 141 GB of weights (estimate; KV cache and activations extra)INT4141 GBFP8: about 283 GB of weights (estimate; KV cache and activations extra)FP8283 GBBF16: about 566 GB of weights (estimate; KV cache and activations extra)BF16566 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 141 GB of weights (estimate; KV cache and activations extra)INT4141 GBFP8: about 283 GB of weights (estimate; KV cache and activations extra)FP8283 GBBF16: about 566 GB of weights (estimate; KV cache and activations extra)BF16566 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2024-06-14), amber triangles are 8 dated articles (each links to it), grey ticks are repo commits.

What people built on it

13 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

13 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

12 quantized (Hugging Face model tree)1 fine-tune (Hugging Face model tree)12 quantized1 fine-tune
12 quantized (Hugging Face model tree)1 fine-tune (Hugging Face model tree)12 quantized1 fine-tune

All variants →

Reference

Everything else, folded: the full tables, every source.

Use-case evidence

3 use cases

What it is good at 3 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

22 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesDeepseekV2ForCausalLM
Model typedeepseek_v2
Dtypebfloat16
Params by dtypeBF16: 235,741,434,880
Size bandXXL
MoEyes
Experts160
Experts per token6
Context length163,840 (config.json max_position_embeddings)
PapersarXiv:2401.06066
LicenseDeepSeek License (deepseek-license) (other)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · DeepseekV2ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · DeepseekV2ForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for DeepSeek-Coder-V2-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)566 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)283 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)141 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

13 variants

Lineage

Fine-tuned from deepseek-ai/DeepSeek-Coder-V2-Base (same organization) Family: deepseek-ai/DeepSeek-V2 13 derivatives on the Hub

Base model: deepseek-ai/DeepSeek-Coder-V2-Base

quantized: 12finetune: 1

Top variants (13 shown, ranked by downloads)

Relation
Quant
13 rows
Variant Relation Quant Params Band License Author Likes Downloads
bartowski/DeepSeek-Coder-V2-Instruct-GGUF quantized GGUF — — other bartowski 36 100.5k
qwp4w3hyb/DeepSeek-Coder-V2-Instruct-iMat-GGUF quantized GGUF — — other qwp4w3hyb 2 78.8k
legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF quantized GGUF — — other legraphista 6 33.6k
second-state/DeepSeek-Coder-V2-Instruct-GGUF quantized GGUF — — other second-state 1 27.7k
gaianet/DeepSeek-Coder-V2-Instruct-GGUF quantized GGUF — — other gaianet 0 9.1k
nisten/deepseek-coder-v2-inst-cpu-optimized-gguf quantized gguf — — — nisten 5 7.6k
mradermacher/DeepSeek-Coder-V2-Instruct-i1-GGUF quantized GGUF — — other mradermacher 7 5.3k
GGorman/DeepSeek-Coder-V2-Instruct-Q4-mlx quantized mlx 236B XXL other GGorman 1 2.5k
nafisabdkhan/DeepSeek-Coder-V2-Instruct-mlx-3Bit quantized mlx 236B XXL other nafisabdkhan 0 2.5k
nafisabdkhan/DeepSeek-Coder-V2-Instruct-mlx-4Bit quantized mlx 236B XXL other nafisabdkhan 0 1.8k
nafisabdkhan/DeepSeek-Coder-V2-Instruct-mlx-2Bit quantized mlx 236B XXL other nafisabdkhan 0 1.5k
Ocean82/deepseek-coder-v2-inst-cpu-optimized-gguf quantized gguf — — — Ocean82 1 1.4k
mradermacher/DeepSeek-Coder-V2-Instruct-GGUF finetune GGUF — — other mradermacher 8 0

Repository changelog

11 commits

Repository changelog 11 commits

DateCommitSHA
2024-08-21 Add base_model metadata (#8) 2453c79
2024-07-03 Update README.md 4aede65
2024-06-24 Update modeling_deepseek.py 601f33f
2024-06-19 Update README.md 5de46fd
2024-06-18 Update README.md 7a1063a
2024-06-18 Update README.md a219823
2024-06-17 Update README.md ba087e2
2024-06-17 Update README.md ee736e8
2024-06-16 Create README.md bc8583c
2024-06-14 Upload folder using huggingface_hub 3cdad23
2024-06-14 initial commit eb68607

Articles and coverage

8 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official1

deepseek.com2024-05-08officialauthorityabout the DeepSeek V2 familyHN 16 pts · 2 comments38 words

Analysis5

youtube.com2026-09-18about the DeepSeek V2 familyHN 1 pts · 0 comments
github.com2024-05-07authorityabout the DeepSeek V2 familyHN 14 pts · 3 comments1,699 words
DeepSeek-V2 is a 236B parameter Mixture-of-Experts model pretrained on 8.1 trillion tokens that saves 42.5% of training costs while boosting maximum generation throughput to 5.76 times. The architecture utilizes MLA (Multi-head Latent Attention) to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference. To utilize DeepSeek-V2 in BF16 format for inference, 80GB*8 GPUs are required.written, reviewed, cited
  • The open-source code runs slower on HuggingFace GPUs compared to the internal codebase due to platform constraints.
  • The SGLang framework supports FP8 KV Cache and offers the best latency and throughput among open-source frameworks for this model.
arxiv.org2025-01-01authorityabout the DeepSeek V2 familyHN 3 pts · 0 comments404 words
DeepSeek-V2 is a strong Mixture-of-Experts language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE, where MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector.written, reviewed, cited
github.com2024-05-08authorityabout the DeepSeek V2 familyHN 7 pts · 0 comments460 words
techwavearena.com2024-11-08about the DeepSeek V2 familyHN 1 pts · 0 comments53 words

Community2

old.reddit.com2024-05-16about the DeepSeek V2 familyHN 21 pts · 0 comments
old.reddit.com2024-07-30about the DeepSeek V2 familyHN 8 pts · 0 comments

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:40 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:11 UTC · Last verified 2026-10-25

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