MODEL ARCHITECTURE SPEC โข DEEPSEEK671B MoE
DeepSeek-R1 (671B MoE) VRAM & inference speed
671B Total Params37B Active (MoE)61 Layers โข 128 KV Heads64K Default Ctx (Max 125K)Ollama: deepseek-r1:671bโก MLA Compressed KV Cache
Accurate memory requirements, KV cache footprint, and tokens/sec generation benchmarks for running DeepSeek-R1 (671B MoE) across local GPUs, Apple Silicon Macs, and cloud instances.
โ OOM482.94 GB (296%)0 t/s
DeepSeek-R1 (671B MoE) on Apple Mac Studio M2 Ultra (192GB Unified Memory)MoE (37B Active / 671B Total)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
482.94 GB
296% of hardware capacity (163 GB)Est. Decode Speed
0 tok/s
37B active params @ 800 GB/sAvailable Headroom
-319.94 GB
Needs +319.94 GB more VRAMMemory Allocation & Headroom
482.94 GB allocated of 163 GB capacity
296% utilized
Weights: 402.6 GBMoE (37B Active)
KV: (32K): 65.5 GBMLA (-85%)
Overhead: 14.84 GB
Deficit: -319.94 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on Apple Mac Studio M2 Ultra (192GB Unified Memory) (800 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
MoE Memory vs Speed Architecture: While decode throughput scales with the active parameter count (37B parameters streamed per token), all 671B parameters must be resident in VRAM so the dynamic router can route tokens to any expert layer.
Multi-Head Latent Attention (MLA): DeepSeek compresses Key-Value projections into a low-rank latent vector, cutting KV memory footprint by ~85% compared to standard MHA/GQA.
โ ๏ธ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek-R1 (671B MoE) @ 482.94 GB VRAM)
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 1342 GB | 65.5 GB | 1455.22 GB (893%) | 0 t/s | OOM |
| Q8_08.5 bpw | 711.26 GB | 65.5 GB | 802.4 GB (492%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 550.22 GB | 65.5 GB | 635.73 GB (390%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 476.41 GB | 65.5 GB | 559.33 GB (343%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 402.6 GB | 65.5 GB | 482.94 GB (296%) | 0 t/s | OOM |
| FP44.25 bpw | 355.63 GB | 65.5 GB | 434.33 GB (266%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 275.11 GB | 65.5 GB | 350.99 GB (215%) | 0 t/s | OOM |
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Hardware Compatibility Matrix for DeepSeek-R1 (671B MoE)
Standard Q4_K_M precision with 32K context window and Q8_0 KV cache
| Hardware System | VRAM Envelope | Required VRAM | Headroom | Est. Decode Speed | Compatibility | Test Fit |
|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 (32GB GDDR7) Consumer GPU โข 1792 GB/s | 32 GB | 482.94 GB | -450.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 4090 (24GB GDDR6X) Consumer GPU โข 1008 GB/s | 24 GB | 482.94 GB | -458.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) Multi-GPU Rig โข 936 GB/s | 48 GB | 482.94 GB | -434.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 5080 (16GB GDDR7) Consumer GPU โข 960 GB/s | 16 GB | 482.94 GB | -466.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA RTX 4070 Ti Super (16GB) Consumer GPU โข 672 GB/s | 16 GB | 482.94 GB | -466.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 3060 (12GB) Consumer GPU โข 360 GB/s | 12 GB | 482.94 GB | -470.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple MacBook Pro/Air M1 (8GB Unified Memory) Apple Silicon โข 68 GB/s | 7 GB | 482.94 GB | -475.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple MacBook Pro/Air (16GB Unified Memory) Apple Silicon โข 100 GB/s | 14 GB | 482.94 GB | -468.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple Mac Mini M4 Pro (64GB Unified Memory) Apple Silicon โข 273 GB/s | 54 GB | 482.94 GB | -428.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple Mac Studio M4 Max (128GB Unified Memory) Apple Silicon โข 546 GB/s | 112 GB | 482.94 GB | -370.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple Mac Studio M5 Ultra (256GB Unified Memory) Apple Silicon โข 890 GB/s | 236 GB | 482.94 GB | -246.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) Unified APU / Mini-PC โข 256 GB/s | 110 GB | 482.94 GB | -372.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA DGX Spark (128GB Grace-Blackwell Unified) AI Appliance โข 273 GB/s | 118 GB | 482.94 GB | -364.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA H100 SXM (80GB HBM3 Cloud GPU) Datacenter Cloud GPU โข 3350 GB/s | 80 GB | 482.94 GB | -402.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 5070 Ti (16GB GDDR7) Consumer GPU โข 896 GB/s | 16 GB | 482.94 GB | -466.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 5070 (12GB GDDR7) Consumer GPU โข 672 GB/s | 12 GB | 482.94 GB | -470.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 4080 Super (16GB GDDR6X) Consumer GPU โข 736 GB/s | 16 GB | 482.94 GB | -466.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| AMD Radeon RX 7900 XTX (24GB GDDR6) Consumer GPU โข 960 GB/s | 24 GB | 482.94 GB | -458.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| AMD Radeon RX 7900 XT (20GB GDDR6) Consumer GPU โข 800 GB/s | 20 GB | 482.94 GB | -462.94 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple Mac Studio M2 Ultra (192GB Unified Memory) Apple Silicon Workstation โข 800 GB/s | 144 GB | 482.94 GB | -338.94 GB | 0 (OOM) | โ OOM | Test Fit โ |