DeepSeek R1 0528 — Hardware Requirements & GPU Compatibility
ChatReasoningFunctions
DeepSeek R1 0528 is an updated release of the R1 reasoning model, incorporating improvements to training and inference that sharpen its performance on complex multi-step problems. It retains the same 684.5 billion parameter mixture-of-experts architecture as the original R1, with approximately 37 billion parameters active per forward pass. This revision addresses several edge cases where the original R1 struggled, delivering more consistent reasoning chains and fewer hallucinations on difficult math and coding tasks. Hardware requirements remain identical to the original R1, so users already set up to run the first version can swap in the 0528 weights with no changes to their infrastructure.
DeepSeek R1 0528 runs on a 512 GB+ Mac or a multi-GPU server
Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 412.2 GB · ~18 tok/s
Macs from 256 GB (IQ2_M · ~28 tok/s)
Score 103.7#49 of 106 open#45 of 102 that fit ≤512 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
103.7
- 90% range 101.0–106.4
- #49 of 106 open models
- #45 of 102 that fit ≤512 GB
Run DeepSeek R1 0528
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.
Recommended
vllm serve deepseek-ai/DeepSeek-R1-0528 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: deepseek-ai_DeepSeek-R1-0528-Q4_K_M/deepseek-ai_DeepSeek-R1-0528-Q4_K_M-00001-of-00011.gguf409.23 GB + 10 partsbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 6× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$16.14/hr
- est., whole server
- GPUs
- 6 GPUs
- 480 GB of VRAM
$5+ bonus for new users
How Much VRAM Does DeepSeek R1 0528 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 294.8 GB | 293.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 5× H100 SXM 80 GB | |
| 303.4 GB | 302.1 GB | 3-bit small — 3-bit small quantization | Runs on 5× H100 SXM 80 GB | |
| 337.6 GB | 336.3 GB | 3-bit medium — 3-bit medium quantization | Runs on 5× H100 SXM 80 GB | |
| 346.1 GB | 344.9 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on 5× H100 SXM 80 GB | |
| 414.6 GB | 413.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 6× H100 SXM 80 GB | |
| 491.6 GB | 490.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 7× H100 SXM 80 GB | |
| 568.6 GB | 567.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 8× H100 SXM 80 GB | |
| 688.4 GB | 687.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× H200 SXM 141 GB |
Selected Q4_K_M
4-bit medium quantization412.2GBat 16K context
- Weights
- 410.7 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Server-class: ~412.2 GB at 16K context · about 6× H100 SXM 80 GB
Which Hardware Can Run DeepSeek R1 0528?
Q4_K_M · 414.6 GBDeepSeek R1 0528 (Q4_K_M) requires 414.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 539+ GB is recommended. Using the full 164K context window can add up to 7.9 GB, bringing total usage to 422.5 GB. No consumer GPU has enough memory. 1 device with unified memory can run DeepSeek R1 0528, including Mac Studio (M3 Ultra, 512GB).
192+ GB
Q4_K_M · best fit IQ2_XXS (189.7 GB)
4 GPUs · 1 Mac class · 2 device families
B300~181Simulate output on B300Instinct MI350X~69Simulate output on Instinct MI350XInstinct MI355X~69Simulate output on Instinct MI355XM3 Ultra256–512 GB~31Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~250Simulate output on NVIDIA DGX H1008x A100 SXM4~226Simulate 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 192 GB: too small for this model (needs ≥ 189.7 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is DeepSeek R1 0528?
The llmrun Score merges 10 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤512 GB tier
- DeepSeek R1 0528
- Other models
- Best Score for its VRAM
#45 of 102 that fit ≤512 GB
Each dot is a model at the best quant that fits ≤512 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤512 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 13 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · May 2025Independent
Independent76.3%
#29/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1421.5
#34/188 open
- AIME 2024/2025
Epoch AI · May 2025Independent
Independent66.4%
#34/88 open
- ARC-AGI-2
Epoch AI · May 2025Independent
Independent1.1%
#19/22 open
- MMLU-Pro
TIGER-LabIndependent
Independent83.4%
#13/146 open
- Aider Polyglot
Epoch AI · May 2025Independent
Independent71.4%
#2/18 open
In the ≤512 GB class
102 models fit ≤512 GBSame Score, only models that fit ≤512 GB at their best quant: this model's peers by memory need.
- #1
DeepSeek V4.1 Flash 1017DeepSeek
134.4
458.4 GB
- #2
MiMo V2.6 Pro RLXiaomi
133.3
499.3 GB
- #3
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
- #4
GLM 5.3Z.ai
130.1
453.8 GB
- #5
DeepSeek V4.1 FlashDeepSeek
129.4
458.4 GB
- #6
Step 5 PreviewStepFun
128.0
407.5 GB
Alternatives to DeepSeek R1 0528
DeepSeek R1 family · 13 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Best alternative at this size
DeepSeek V4.1 Flash 1017
DeepSeek
- 30.7 higher Score
- 46.3 GB more VRAM
Score 134.4 · 458.4 GB
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 20.4 higher Score
- 207 GB less VRAM
Score 124.1 · 205.2 GB
Other vendor
GLM 5.3
Z.ai
- 26.4 higher Score
- 41.6 GB more VRAM
Score 130.1 · 453.8 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4.1 FlashDeepSeek | 129.4 | 458.4 GB | —needs > 24 GB | similar score |
| Step 5 PreviewStepFun | 128.0 | 407.5 GB | —needs > 24 GB | similar score |
| GLM 5.2Z.ai | 122.9 | 453.8 GB | —needs > 24 GB | similar score |
| Beam 1.0 501B A23Breflection | 119.5 | 304 GB | —needs > 24 GB | similar score |
| GLM 5.1Z.ai | 117.8 | 454.1 GB | —needs > 24 GB | similar score |
| Qwen3.5 397B A17BAlibaba | 112.8 | 242.6 GB | —needs > 24 GB | similar score |
| MiniMax M3MiniMax | 112.6 | 258 GB | —needs > 24 GB | similar score |
| GLM 4.7Z.ai | 109.1 | 217.9 GB | —needs > 24 GB | similar score |
| MiniMax M2MiniMax | 104.0 | 139.6 GB | —needs > 24 GB | similar score |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | similar score |
Fine-tunes & variants(1)
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek R1
- Parameters
- 684.5B
- Active parameters
- 40.1B per token
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2025-05-28
- License
- MIT
Frequently Asked Questions
How much VRAM does DeepSeek R1 0528 need?
DeepSeek R1 0528 requires 414.6 GB of VRAM at Q4_K_M, or 1372.9 GB at BF16. Full 164K context adds up to 7.9 GB (422.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 684.5B × 4.8 bits ÷ 8 = 410.7 GB
KV Cache + Overhead ≈ 3.9 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 ≈ 11.8 GB (at full 164K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run DeepSeek R1 0528?
No — DeepSeek R1 0528 requires at least 192.1 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for DeepSeek R1 0528?
For DeepSeek R1 0528, Q4_K_M (414.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (474.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 192.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek R1 0528 on a Mac?
Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac can run DeepSeek R1 0528, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 414.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 512GB).
Can I run DeepSeek R1 0528 locally?
Yes — DeepSeek R1 0528 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of DeepSeek R1 0528?
At Q4_K_M, the download is about 410.72 GB. The full-precision BF16 version is 1369.06 GB. The smallest option (IQ2_XXS) is 188.25 GB.
Which GPUs can run DeepSeek R1 0528?
No single consumer GPU has enough VRAM to run DeepSeek R1 0528 at Q4_K_M (414.6 GB). Multi-GPU or professional hardware is required.
Which devices can run DeepSeek R1 0528?
1 device with unified memory can run DeepSeek R1 0528 at Q4_K_M (414.6 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.