Deepseek Llm 67B Chat — Hardware Requirements & GPU Compatibility
Chat
Deepseek Llm 67B Chat is a 67B-parameter open language model from DeepSeek in the DeepSeek family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 41.30 GB of VRAM — see which GPUs and Macs can run it below.
Doesn't fit a 24 GB GPU
Smallest consumer fit → RTX 5090 · Q2_K · 30.4 GB · ~39 tok/s
Macs from 36 GB (Q2_K · ~11 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Deepseek Llm 67B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run deepseek-llm:67b-chat-q4_K_MQ4_K_M needs offload on the RTX 4090 · 18.1 GB short · 4K context
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on A100 80GB (37 GB headroom) · Q4_K_M
- Generation speed
- ~32 tok/s
- generation speed
- Cost per 1M output tokens
- $10.02
- per 1M output tokens
How Much VRAM Does Deepseek Llm 67B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 29.6 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 29.6 GB | |
| 33.8 GB | — | 3-bit medium — 3-bit medium quantization | Does not fit: needs 33.8 GB | |
| 41.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 41.3 GB | |
| 48.8 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 48.8 GB | |
| 56.4 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 56.4 GB | |
| 68.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 68.1 GB | |
| 135.1 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 135.1 GB |
Selected Q4_K_M
4-bit medium quantization42.1GBat 4K context
- Weights
- 40.2 GB
- KV cache
- 1.6 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 18.1 GB more
Which Hardware Can Run Deepseek Llm 67B Chat?
Q4_K_M · 41.3 GBDeepseek Llm 67B Chat (Q4_K_M) requires 41.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 54+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 42.1 GB. No consumer GPU has enough memory. 67 devices with unified memory can run Deepseek Llm 67B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
32–40 GB
Q4_K_M · best fit Q2_K (30.4 GB)
1 GPU · 2 Mac classes · 1 device family
RTX 5090~39Simulate output on RTX 5090M5 Max36 GB~11Simulate output on Mac Studio M5 Max (36 GB)M3 Pro*36 GB~4Simulate output on MacBook Pro 14" M3 Pro (36 GB)Orin~5Simulate output on NVIDIA Jetson AGX Orin 32GB48 GB
48 GB tier →fits · 5.9 GB free · up to 4K ctx
2 Mac classes · 1 device family
M5 Max48 GB~10Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~5Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~4Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Deepseek Llm 67B Chat?
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
Here are all 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Nov 2023Independent
Independent24.6%
#90/98 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1185.1
#150/187 open
- AIME 2024/2025
Epoch AI · Nov 2023Independent
Independent0.8%
#80/86 open
- MATH Level 5
Epoch AI · Nov 2023Independent
Independent6.4%
#35/39 open
Alternatives to Deepseek Llm 67B Chat
DeepSeek family · 5 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 3.3 70B InstructMeta | 79.7 | 51.3 GB | —needs > 24 GB | same size |
| Qwen1.5 72B ChatAlibaba | — | 86.6 GB | —needs > 24 GB | same size |
| CodeLlama 70B Instruct HFMeta | — | 23.2 GB | ~29 | same size |
| StableBeluga2Stability AI | — | 23.5 GB | ~29 | same size |
| Apertus 70B 2509swiss-ai | — | 48 GB | —needs > 24 GB | same size |
| Meditron3 70BEPFLiGHT | — | 159.9 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek
- Parameters
- 67B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2023-11-29
- License
- Other
Frequently Asked Questions
How much VRAM does Deepseek Llm 67B Chat need?
Deepseek Llm 67B Chat requires 41.3 GB of VRAM at Q4_K_M, or 135.1 GB at BF16. Full 4K context adds up to 0.8 GB (42.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 67B × 4.8 bits ÷ 8 = 40.2 GB
KV Cache + Overhead ≈ 1.1 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 ≈ 1.9 GB (at full 4K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Deepseek Llm 67B Chat?
Yes, at Q2_K (29.6 GB) or lower. Higher quantizations like Q3_K_M (33.8 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
What's the best quantization for Deepseek Llm 67B Chat?
For Deepseek Llm 67B Chat, Q4_K_M (41.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (48.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 29.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Deepseek Llm 67B Chat on a Mac?
Yes — Mac Studio M5 Max (36 GB) and 65 other Macs can run Deepseek Llm 67B Chat, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 41.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).
Can I run Deepseek Llm 67B Chat locally?
Yes — Deepseek Llm 67B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Deepseek Llm 67B Chat?
At Q4_K_M, Deepseek Llm 67B Chat can reach ~116 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 41.3 × 0.65 = ~126 tok/s
Estimated speed at Q4_K_M (41.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Deepseek Llm 67B Chat?
At Q4_K_M, the download is about 40.20 GB. The full-precision BF16 version is 134.00 GB. The smallest option (Q2_K) is 28.48 GB.
Which GPUs can run Deepseek Llm 67B Chat?
No single consumer GPU has enough VRAM to run Deepseek Llm 67B Chat at Q4_K_M (41.3 GB). Multi-GPU or professional hardware is required.
Which devices can run Deepseek Llm 67B Chat?
67 devices with unified memory can run Deepseek Llm 67B Chat at Q4_K_M (41.3 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.