DeepSeek v2 Chat — Hardware Requirements & GPU Compatibility
Chat
DeepSeek v2 Chat is a 235.7B-parameter open language model from DeepSeek in the DeepSeek V2 family. It supports a context window of up to 163,840 tokens. At Q4_K_M it needs about 144.26 GB of VRAM — see which GPUs and Macs can run it below.
DeepSeek v2 Chat 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
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run DeepSeek v2 Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve deepseek-ai/DeepSeek-V2-Chat --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits 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
How Much VRAM Does DeepSeek v2 Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 103.0 GB | 102.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on Instinct MI250X 128 GB | |
| 117.7 GB | 117.5 GB | 3-bit medium — 3-bit medium quantization | Runs on Instinct MI250X 128 GB | |
| 144.3 GB | 144 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on GH200 Grace Hopper Superchip 144 GB | |
| 170.8 GB | 170.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on B200 192 GB | |
| 197.3 GB | 197.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on Instinct MI325X 256 GB | |
| 238.6 GB | 238.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on Instinct MI325X 256 GB | |
| 474.3 GB | 474.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 7× H100 SXM 80 GB |
Selected Q4_K_M
4-bit medium quantization142.9GBat 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
Which Hardware Can Run DeepSeek v2 Chat?
Q4_K_M · 144.3 GBDeepSeek v2 Chat (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. 6 devices with unified memory can run DeepSeek v2 Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).
128–144 GB
Q4_K_M · best fit Q3_K_M (116.4 GB)
4 GPUs · 2 Mac classes · 1 device family
GH200 Grace Hopper Superchip~150Simulate output on GH200 Grace Hopper SuperchipH200 NVL~149Simulate output on H200 NVLH200 SXM~149Simulate output on H200 SXMM2 Ultra*128 GB~32Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~27Simulate output on Mac Studio M5 Max (128 GB)GB10~16Simulate output on NVIDIA DGX Spark192+ GB
fits · 49.1 GB free · up to 160K ctx
6 GPUs · 1 Mac class · 2 device families
B200~172Simulate output on B200B300~172Simulate output on B300Instinct MI350X~68Simulate output on Instinct MI350XM3 Ultra*256–512 GB~29Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~247Simulate output on NVIDIA DGX H1008x A100 SXM4~221Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 96 GB: too small for this model (needs ≥ 101.6 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Alternatives to DeepSeek v2 Chat
DeepSeek V2 family · 5 models →Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniMax M2.7MiniMax | 106.8 | 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 |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek V2
- Parameters
- 235.7B
- Active parameters
- 21.4B per token
- Architecture
- DeepseekV2ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2024-04-28
- License
- Other
Frequently Asked Questions
How much VRAM does DeepSeek v2 Chat need?
DeepSeek v2 Chat 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
Can NVIDIA GeForce RTX 5090 run DeepSeek v2 Chat?
No — DeepSeek v2 Chat requires at least 103.0 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for DeepSeek v2 Chat?
For DeepSeek v2 Chat, Q4_K_M (144.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (170.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 103.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek v2 Chat on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run DeepSeek v2 Chat is Mac Pro M2 Ultra (128 GB) at Q2_K; 15 of the 156 Macs we list can run it at some quantization. For Q4_K_M (144.3 GB) you need a Mac with more unified memory.
Can I run DeepSeek v2 Chat locally?
Yes — DeepSeek v2 Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is DeepSeek v2 Chat?
At Q4_K_M, DeepSeek v2 Chat 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)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of DeepSeek v2 Chat?
At Q4_K_M, the download is about 141.44 GB. The full-precision BF16 version is 471.48 GB. The smallest option (Q2_K) is 100.19 GB.
Which GPUs can run DeepSeek v2 Chat?
No single consumer GPU has enough VRAM to run DeepSeek v2 Chat at Q4_K_M (144.3 GB). Multi-GPU or professional hardware is required.
Which devices can run DeepSeek v2 Chat?
6 devices with unified memory can run DeepSeek v2 Chat 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.