DeepSeekDeepSeek CoderDeepseekV2ForCausalLM

DeepSeek Coder v2 Instruct — Hardware Requirements & GPU Compatibility

ChatCode

DeepSeek-Coder-V2-Instruct is DeepSeek's mixture-of-experts code and math model, continuing pretraining from an intermediate DeepSeek-V2 checkpoint with an additional 6 trillion tokens focused on code. It has about 235.7 billion total parameters with roughly 21.4 billion active per token, and DeepSeek reports coding and mathematical reasoning performance comparable to GPT-4 Turbo, expanding supported programming languages from 86 to 338 versus the original DeepSeek-Coder line. It is an instruction-tuned chat model, not a base model, intended for code generation, completion, and reasoning tasks. Given its total size, DeepSeek recommends an eight-GPU, 80GB-class server for BF16 inference, so it needs a substantial multi-GPU setup even once quantized. Context length is 163,840 tokens; DeepSeek's own materials describe this as an extension "from 16K to 128K" over the original Deepseek Coder. Model weights are released under DeepSeek's custom model license, which permits commercial use, while the surrounding code is MIT-licensed. It was published in June 2024.

235.7B params · MoE21B active164K contextOtherJun 20243.8K downloads718 likes5.4K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

DeepSeek Coder v2 Instruct runs on a 192 GB+ Mac or a datacenter GPU

Best local pick → Mac Pro M2 Ultra (192 GB) · Q4_K_M · 142.9 GB · ~28 tok/s

Macs from 96 GB (IQ2_XS · ~42 tok/s)

Other

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run DeepSeek Coder v2 Instruct

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on B200 (192 GB) (49 GB headroom) · Q4_K_M

Generation speed: ~172 tok/s; generation speed
Cost per 1M output tokens: $9.66; per 1M output tokens

Deployon RunPodBest valuefrom $5.98/hr

$5+ bonus for new users

Compare GPUs →

Run DeepSeek Coder v2 Instruct

142.9 GB VRAM at 16K context · runs on one GH200 Grace Hopper Superchip 144 GB

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf bartowski/DeepSeek-Coder-V2-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/bartowski/DeepSeek-Coder-V2-Instruct-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf bartowski/DeepSeek-Coder-V2-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1. Splits the model over 8 GPUs of one server (tensor parallel).
$vllm serve deepseek-ai/DeepSeek-Coder-V2-Instruct --max-model-len 16384 --tensor-parallel-size 8
Runs the original weights, not the Q4_K_M GGUF; needs ~519 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model GGorman/DeepSeek-Coder-V2-Instruct-Q4-mlx
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model GGorman/DeepSeek-Coder-V2-Instruct-Q4-mlx

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$bartowski/DeepSeek-Coder-V2-Instruct-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
B200 (192 GB)CheapestCheapest49 GB free	~172 tok/s	$9.66	Deploy on RunPodfrom $5.98/hr (opens in new tab)	192 GB · 49 GB free · ~172 tok/s · $9.66 / 1M tokens
B300 (288 GB)145 GB free	~172 tok/s	$11.21	Deploy on RunPodfrom $6.94/hr (opens in new tab)	288 GB · 145 GB free · ~172 tok/s · $11.21 / 1M tokens
B200 (192 GB)49 GB free	~172 tok/s	$12.12	Deploy on Vast.aifrom $7.50/hr (opens in new tab)	192 GB · 49 GB free · ~172 tok/s · $12.12 / 1M tokens

Compare GPUs →

How Much VRAM Does DeepSeek Coder v2 Instruct Need?

Select a quantization level; the listed hardware adheres to it.

VRAM	+32K	+128K	File	Quality	Runs on
73.5 GB	73.3 GB	80.1 GB	68.7 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Runs on H100 SXM 80 GB
103.0 GB	102.8 GB	109.6 GB	85.9 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Runs on Instinct MI250X 128 GB
106.0 GB	105.7 GB	112.5 GB	101.7 GB	3-bit small — 3-bit small quantization	Runs on Instinct MI250X 128 GB
117.7 GB	117.5 GB	124.3 GB	112.7 GB	3-bit medium — 3-bit medium quantization	Runs on Instinct MI250X 128 GB
120.7 GB	120.4 GB	127.2 GB	132.9 GB	4-bit · legacy — 4-bit legacy quantization	Runs on Instinct MI250X 128 GB
123.6 GB	123.4 GB	130.2 GB	122.4 GB	3-bit large — 3-bit large quantization	Runs on Instinct MI250X 128 GB
135.4 GB	135.2 GB	142 GB	133.9 GB	4-bit small — 4-bit small quantization	Runs on H200 NVL 141 GB
144.3 GB	144 GB	150.8 GB	142.5 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Runs on GH200 Grace Hopper Superchip 144 GB
150.2 GB	149.9 GB	156.7 GB	162.3 GB	5-bit · legacy — 5-bit legacy quantization	Runs on B200 192 GB
164.9 GB	164.6 GB	171.4 GB	162.3 GB	5-bit small — 5-bit small quantization	Runs on B200 192 GB
170.8 GB	170.5 GB	177.3 GB	167.2 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Runs on B200 192 GB
197.3 GB	197.1 GB	203.8 GB	193.5 GB	6-bit, very good — 6-bit quantization, very good quality	Runs on Instinct MI325X 256 GB
238.6 GB	238.3 GB	245.1 GB	250.6 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Runs on Instinct MI325X 256 GB
474.3 GB	474.1 GB	480.8 GB	~471.5 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Runs on 7× H100 SXM 80 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

142.9GBat 16K context

Weights: 141.4 GB
KV cache: 1.1 GB
Overhead: 0.3 GB

Server-class: ~142.9 GB at 16K context · one GH200 Grace Hopper Superchip 144 GB

Or .

Which Hardware Can Run DeepSeek Coder v2 Instruct?

Q4_K_M · 144.3 GB

DeepSeek Coder v2 Instruct (Q4_K_M) requires 144.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 188+ GB is recommended. Using the full 164K context window can add up to 8.8 GB, bringing total usage to 153.1 GB. No consumer GPU has enough memory. 4 devices with unified memory can run DeepSeek Coder v2 Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (192 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 72.2 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

How good is DeepSeek Coder v2 Instruct?

llmrun Score blends public benchmark boards into one number; 100 = the average open model.

Not enough data yet for an llmrun Score.

A Score needs results on enough independent boards. How the Composite Score is built

Benchmarks

LMArena Text
LMArena · Oct 2026Arena
1265.4
#126/188 open
Arena
MMLU-Pro
TIGER-LabIndependent
63.6%
#51/146 open
Independent
GSM8K
Epoch AI · Jun 2024Lab-reported
94.5%
#1/62 open
Lab-reported

All 3 DeepSeek Coder v2 Instruct benchmarks

Alternatives to DeepSeek Coder v2 Instruct

DeepSeek Coder family · 11 models →

Similar models

Model	Score	VRAM	tok/s	Why it is similar
MiniMax M2.7MiniMax	107.4	139.6 GB	—needs > 24 GB	same size
Command A Plus 05 2026 BF16Cohere	—	132.1 GB	—needs > 24 GB	same size
Qwen3 VL 235B A22B InstructAlibaba	—	143.3 GB	—needs > 24 GB	same size
Laguna M.1poolside	—	138.1 GB	—needs > 24 GB	same size
Llama 3 1 Nemotron Ultra 253B V1NVIDIA	—	564.2 GB	—needs > 24 GB	same size
DeepSeek V2.5 1210DeepSeek	—	142.9 GB	—needs > 24 GB	same size

Specifications

Publisher: DeepSeek
Family: DeepSeek Coder
Parameters: 235.7B
Active parameters: 21.4B per token
Architecture: DeepseekV2ForCausalLM
Context Length: 163,840 tokens
Vocabulary Size: 102,400
Release Date: 2024-06-14
License: Other

Resources

Frequently Asked Questions

How much VRAM does DeepSeek Coder v2 Instruct need?

DeepSeek Coder v2 Instruct requires 144.3 GB of VRAM at Q4_K_M, or 474.3 GB at BF16. Full 164K context adds up to 8.8 GB (153.1 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 235.7B × 4.8 bits ÷ 8 = 141.4 GB

KV Cache + Overhead ≈ 2.9 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 11.7 GB (at full 164K context)

VRAM usage by quantization

Q4_K_M

144.3 GB

Q4_K_M + full context

153.1 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run DeepSeek Coder v2 Instruct?

No — DeepSeek Coder v2 Instruct requires at least 73.5 GB at IQ2_XS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for DeepSeek Coder v2 Instruct?

For DeepSeek Coder v2 Instruct, Q4_K_M (144.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (150.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 73.5 GB.

VRAM requirement by quantization

IQ2_XS

73.5 GB~57%

Q3_K_M

117.7 GB~83%

Q4_K_M ★

144.3 GB~89%

Q5_0

150.2 GB~90%

Q5_K_M

170.8 GB~92%

BF16

474.3 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run DeepSeek Coder v2 Instruct on a Mac?

Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run DeepSeek Coder v2 Instruct, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 144.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (192 GB).

Can I run DeepSeek Coder v2 Instruct locally?

Yes — DeepSeek Coder v2 Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is DeepSeek Coder v2 Instruct?

At Q4_K_M, DeepSeek Coder v2 Instruct can reach ~68 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)

Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.

Example: NVIDIA B200 → 13.1 GB active ÷ (8000 × 0.65) = 2.52 ms, plus 60 layers × 0.055 ms = 3.30 ms, so 1000 ÷ 5.82 ms = ~172 tok/s

Estimated speed at Q4_K_M (144.3 GB)

NVIDIA B200

~172 tok/s

NVIDIA B300

~172 tok/s

AMD Instinct MI350X

~68 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of DeepSeek Coder v2 Instruct?

At Q4_K_M, the download is about 141.44 GB. The full-precision BF16 version is 471.48 GB. The smallest option (IQ2_XS) is 70.72 GB.

Which GPUs can run DeepSeek Coder v2 Instruct?

No single consumer GPU has enough VRAM to run DeepSeek Coder v2 Instruct at Q4_K_M (144.3 GB). Multi-GPU or professional hardware is required.

Which devices can run DeepSeek Coder v2 Instruct?

4 devices with unified memory can run DeepSeek Coder v2 Instruct at Q4_K_M (144.3 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.