DeepSeek R1-0528 671B on MTT S4000 48GB (摩尔线程) (48GB) — VRAM, KV Cache & Throughput
DeepSeek R1-0528 671B (671B total, 37B active, Multi-head Latent Attention (MLA) MoE) uses 377 GiB of 4-bit weights (Q4_K_M), requiring 378.11 GiB total at 8K context and 379.71 GiB at 32K on MTT S4000 48GB (摩尔线程) (~2.7 tok/s decode).
Exceeds Available GPU Memory
Measured GGUF shard size
HF config.json ↗
Required GPU Memory
378.11 GiB
405.99 GB (decimal)
Usable Hardware Memory
45.6 GiB
48 GB (physical)
Safe Headroom
-332.51 GiB
788% utilization
Est. Decode Throughput
~2.7 tok/s
37B active params
Est. Prefill Throughput
~703 tok/s
4K Prompt TTFT: ~5829 ms
Est. Cost / 1M Tokens
$363.79
Multi-head Latent Attention (MLA) MoE · MIT
Recommended Hardware for Current Configuration
Best Single GPU
Multi-GPU Required
—
Best Value / Used GPU
4x H200 141GB SXM
564GB · ~$128,000
Apple Unified Memory
Mac Studio M3 Ultra 512GB
~$9,500
Cloud From
Vast.ai · $3.54 / hr
8x A100 80GB SXM
Production Launch Commands
llama.cpp (GGUF)
llama-server -hf unsloth/DeepSeek-R1-0528-GGUF:Q4_K_M \ --ctx-size 8192 -ngl 999 --flash-attn
vLLM
vllm serve deepseek-ai/DeepSeek-R1-0528 \ --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~5829ms · ~2.7 tok/s)
[DeepSeek R1-0528 671B · 378.11 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...
Cloud GPU Comparison & Rent-vs-Buy TCO
Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.
Purchase break-even
~2.3 months
Est. local power: $6.38 / mo
| Provider | Recommended Instance | Total VRAM | Hourly Rate | Monthly at Selected Hours | Billing Mode | Deploy |
|---|---|---|---|---|---|---|
| RunPod | 2x B300 288GB Blackwell Ultra (Community Cloud) | 576 GB | $13.88 / hr | $1687.81 / mo | Per-second | Deploy GPU ↗ |
| Vast.ai | 8x A100 80GB SXM (Verified Host · SXM4) | 640 GB | $3.54 / hr | $429.98 / mo | Spot / On-demand | Deploy GPU ↗ |
| Vultr ($300 Free Credit*) | 8x A100 80GB SXM (Dedicated Instance · $300 Credit*) | 640 GB | $19.18 / hr | $2331.80 / mo | Hourly | Deploy GPU ↗ |
| Vultr Cloud GPU | 8x A100 80GB SXM (Dedicated Instance) | 640 GB | $19.18 / hr | $2331.80 / mo | Hourly | Deploy GPU ↗ |
| DigitalOcean | 8x Instinct MI300X 192GB (Dedicated Instance) | 1536 GB | $20.72 / hr | $2519.55 / mo | Per-second | Deploy GPU ↗ |
Rates reflect public listings on the verification date and fluctuate with regional inventory.
Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — DeepSeek R1-0528 671B
QLoRA (4b)
Exceeds Available GPU Memory
383.35 GiB
Model Weights: 377 GiB · Gradients + Optimizer: 5.18 GiB · Act: 1.17 GiB
Recommended GPU: 4x H200 141GB SXM (564GB)
LoRA (16b)
Exceeds Available GPU Memory
1259.71 GiB
Model Weights: 1250 GiB · Gradients + Optimizer: 8.14 GiB · Act: 1.57 GiB
Recommended GPU: 8x B200 180GB (1440GB)
Full BF16
Exceeds Available GPU Memory
5127.77 GiB
Model Weights: 1250 GiB · Gradients + Optimizer: 3875 GiB · Act: 2.77 GiB
Recommended GPU: Multi-GPU Required
Engineering Sizing Notes & Optimization Playbook
Quick Hardware Verdict
A single MTT S4000 48GB (摩尔线程) (48GB) cannot hold 4-bit DeepSeek R1-0528 671B (378.11 GiB at 8K) entirely in VRAM. Use multi-GPU tensor parallelism, offload layers to system RAM, or deploy on a larger cloud GPU.
KV Cache & Runtime Tuning
DeepSeek R1-0528 671B uses Multi-head Latent Attention (MLA) MoE (671B total, 37B active per token). Moving from 8K to 32K context expands FP16 KV cache from 0.54 GiB to 2.14 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 1.07 GiB at 32K (reducing total VRAM to 378.64 GiB).
VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)
Quantization Precision & Memory Matrix — DeepSeek R1-0528 671B @ 8K
| Quantization Format | Weights (GiB) | KV Cache | Total VRAM (GiB) | Decode Speed | Compatibility (MTT S4000 48GB (摩尔线程)) | Recommended GPU | Data Source |
|---|---|---|---|---|---|---|---|
| Native Weights (BF16 / FP8 / MXFP4 — Lossless) (FP8) | 1250 GiB | 0.54 GiB | 1251.11 GiB (1343.37 GB) | ~0.8 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Official checkpoint size |
| FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) | 664 GiB | 0.54 GiB | 665.11 GiB (714.16 GB) | ~1.5 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Measured GGUF shard size |
| GGUF Q6_K (6-bit — High Precision) (Q6_K) | 513 GiB | 0.54 GiB | 514.11 GiB (552.02 GB) | ~1.9 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Measured GGUF shard size |
| GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) | 443 GiB | 0.54 GiB | 444.11 GiB (476.86 GB) | ~2.3 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Measured GGUF shard size |
| Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) | 377 GiB | 0.54 GiB | 378.11 GiB (405.99 GB) | ~2.7 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Measured GGUF shard size |
| GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) | 333.5 GiB | 0.54 GiB | 334.61 GiB (359.28 GB) | ~3.1 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Measured GGUF shard size |
| GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) | 298 GiB | 0.54 GiB | 299.11 GiB (321.17 GB) | ~3.5 tok/s | Exceeds Available GPU Memory | Multi-GPU Required | Measured GGUF shard size |
| GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) | 229.5 GiB | 0.54 GiB | 230.61 GiB (247.62 GB) | ~4.7 tok/s | Exceeds Available GPU Memory | Instinct MI325X 256GB (256GB) | Measured GGUF shard size |
Technical Specifications & FAQ
Does DeepSeek R1-0528 671B fit on MTT S4000 48GB (摩尔线程) (48GB)?
In 4-bit (Q4_K_M) at 8,192 context, DeepSeek R1-0528 671B requires 378.11 GiB total GPU memory (377 GiB weights + 0.54 GiB KV cache + 0.57 GiB runtime buffers) against 45.6 GiB usable on MTT S4000 48GB (摩尔线程), delivering ~2.7 tok/s decode.
How much VRAM does DeepSeek R1-0528 671B need at 32K context?
At 32,768 context tokens, DeepSeek R1-0528 671B (Multi-head Latent Attention (MLA) MoE) allocates 2.14 GiB for KV cache and state, bringing total 4-bit VRAM to 379.71 GiB (407.71 GB decimal).
Evaluate This Model on Other GPUs
RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 5060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 4090 D (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A800 80GB SXM (80GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H20 141GB HBM3e (141GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)MetaX C500 64GB (沐曦曦云) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Ryzen AI Max+ 395 128GB (128GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Arc Pro B60 24GB (24GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)
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