DeepSeek-V4 VRAM Requirements - Million-Token Local Inference Guide
DeepSeek-V4-Pro and DeepSeek-V4-Flash hardware guide: practical VRAM estimates, 1M context implications, Think Max memory planning, multi-GPU setups, and Mac guidance.
DeepSeek-V4 is not a normal "will it fit on my GPU?" release. It is a long-context reasoning system aimed at million-token agent workflows.
The official model card describes local inference support and recommends temperature = 1.0, top_p = 1.0; for Think Max reasoning mode it recommends at least a 384K-token context window. Source: deepseek-ai/DeepSeek-V4-Pro on Hugging Face.
That context recommendation is the important part. With DeepSeek-V4, KV cache can dominate your memory budget.
Quick Answer
- Single consumer GPU: no, not for full DeepSeek-V4.
- RTX 4090 / RTX 5090: useful only for distills, partial offload, or small Flash experiments.
- 2x 48GB GPUs: experimental with aggressive quantization and limited context.
- 4x 80GB GPUs: realistic for serious local inference.
- 192-256 GB Apple Silicon / unified memory: possible for quantized experiments, slower than high-end multi-GPU.
- 384K-1M context: server/workstation territory.
If you just want a DeepSeek model that runs locally today, use DeepSeek R1 GPU Requirements or DeepSeek R1 VRAM Requirements instead.
Weight Memory Estimates
DeepSeek-V4-Pro is reported as a trillion-scale MoE-class model. Exact deployed memory depends on the released checkpoint, expert layout, and quantization format. For planning, use conservative ranges:
| Deployment | Weight memory target | Practical hardware |
|---|---|---|
| Aggressive 2-3 bit quant | 250-450 GB | Multi-GPU or very large unified memory |
| 4-bit quant | 500-850 GB | Server-class multi-GPU |
| 8-bit quant | 1 TB+ | Multi-node/server |
| BF16 | Multiple TB | Datacenter only |
These are not "parameter count × bytes" exact claims. They are deployment planning bands for avoiding the common mistake: assuming MoE active parameters determine memory. They do not. All experts still need to be stored unless the runtime supports expert streaming/offload.
KV Cache Is the Real Problem
DeepSeek-V4 is built for million-token context. At 384K tokens, KV cache can add tens to hundreds of GB depending on architecture, precision, number of layers, and whether the runtime uses cache compression.
Practical tiers:
| Context target | Hardware implication |
|---|---|
| 32K | Already large, but possible with quantized weights and offload |
| 128K | High-memory workstation |
| 384K Think Max | Multi-GPU/server target |
| 1M | Specialized deployment, not consumer local AI |
If your workflow only needs 16K-32K context, a smaller model will usually beat DeepSeek-V4 on latency and hardware cost. See Qwen3.6-27B VRAM Requirements for the consumer-GPU coding alternative.
Hardware Recommendations
Consumer GPUs
An RTX 4090, RTX 5090, RX 7900 XTX, or RTX 6000-class 24-32 GB card is not enough for full DeepSeek-V4.
Use these GPUs for:
- DeepSeek R1 distills
- Smaller DeepSeek-V4-Flash derivatives if released in quantized form
- CPU/RAM offload experiments
- Serving a retrieval or tool stack around a smaller reasoning model
For 24GB hardware, compare best AI models for 24GB VRAM.
Multi-GPU Workstations
DeepSeek-V4 starts to make sense when you have aggregate VRAM in the hundreds of GB.
- 2x 48GB: experimental only, context-limited.
- 4x RTX 6000 Ada 48GB: plausible for aggressive quantization and shorter context.
- 4x A100/H100 80GB: realistic baseline for serious local use.
- 8x 80GB: better target for long context and production latency.
Runtime support matters. Tensor parallelism, expert parallelism, KV cache precision, and CPU offload can change whether a deployment is merely possible or actually usable.
Apple Silicon
Large unified-memory Macs can run models that consumer GPUs cannot fit, but bandwidth and runtime maturity matter.
| Mac memory | DeepSeek-V4 role |
|---|---|
| 64 GB | No, use smaller DeepSeek/Qwen/Gemma models |
| 128 GB | Distills and small Flash experiments |
| 192 GB | Aggressive quant/offload experiments |
| 256 GB | Best Apple Silicon chance, still context-limited |
For most Mac users, Qwen3.6-27B, Gemma 4, or MiniMax M2.7 are more practical.
DeepSeek-V4 vs Practical Local Models
| Model | Practical local tier | Why pick it |
|---|---|---|
| DeepSeek-V4-Pro | 4x 80GB+ | Million-token reasoning, agentic long-context work |
| DeepSeek-V4-Flash | TBD high-memory | Faster/lighter V4-class workflows |
| DeepSeek R1 32B distill | 24 GB | Strong reasoning on consumer GPU |
| Qwen3.6-27B | 24 GB | Best local coding footprint |
| Gemma 4 26B-A4B | 16-24 GB | Fast MoE reasoning, Apache 2.0 |
| Granite 4.1 30B | 24 GB | Enterprise-friendly dense model |
Recommendation
Do not buy a single consumer GPU expecting to run DeepSeek-V4 well. Buy for the smaller model you will actually use every day, then treat DeepSeek-V4 as a server/workstation option.
If you need local long-context coding today:
- 24 GB: Qwen3.6-27B Q4/Q6
- 32-48 GB: Qwen3.6 with larger quant or Granite 4.1 30B
- 128 GB unified memory: MiniMax M2.7 or larger MoE experiments
- 320 GB+ VRAM: DeepSeek-V4 becomes realistic
Use the VRAM calculator to compare your exact GPU or Mac against realistic local models before optimizing for DeepSeek-V4.