DeepSeek V4 Flash Hardware Requirements

Direct answer

Direct answer guidance: Plan at least 192 GB fast accelerator memory for a stable quantized deployment and 256 GB for safer cache/concurrency headroom.

Model / quant variants

Model / quant variants guidance: Use an official/model-specific vLLM or SGLang recipe with supported expert and low-precision kernels. Generic loading/offload can work but is unlikely to deliver a useful service.

Approximate weight footprint

Approximate weight footprint guidance: At 284B total parameters, idealized 4-bit weights alone are about 142 GB. Mixed FP4/FP8 metadata, scales, cache, and workspaces push the practical budget higher.

Hardware tiers

Hardware tiers guidance: At 284B total parameters, idealized 4-bit weights alone are about 142 GB. Mixed FP4/FP8 metadata, scales, cache, and workspaces push the practical budget higher. Mistral Small 4 and gpt-oss-120b fit in 80–128 GB-class systems; Qwen3.6/3.8 fits consumer/pro cards and delivers far lower latency.

Observed configurations

Observed configurations guidance: One optimized Spark example near 14 tok/s demonstrates feasibility. A two-node or high-bandwidth server may improve throughput, but scaling must be measured.

Context / concurrency effects

Context / concurrency effects guidance: Start at 8K–16K. Very long context competes with an already large weight footprint and can create impractical TTFT.

Performance expectations

Performance expectations guidance: One optimized Spark example near 14 tok/s demonstrates feasibility. A two-node or high-bandwidth server may improve throughput, but scaling must be measured.

Best-value configurations

Best-value configurations guidance: Hardware, storage, download time, and operator complexity are all materially higher than a 27B–120B deployment. Confirm the model wins your tasks first. Plan at least 192 GB fast accelerator memory for a stable quantized deployment and 256 GB for safer cache/concurrency headroom.

What will not fit / weak evidence

• Do not size resident memory from active parameters; every expert normally remains stored even when only a subset computes each token.
• A checkpoint described as FP4 may still contain higher-precision tensors and scales.

Planner CTA

Compare this recommendation against your model, context, concurrency, latency, and budget in the ComputeSage Planner.

Sources / methodology

Recommendations combine official specifications, public model/runtime documentation, adjacent benchmark observations, and clearly labeled engineering estimates. Review the ComputeSage evidence methodology.

• DeepSeek-V4-Flash model card
• NVIDIA DGX Spark specifications
• vLLM installation and hardware documentation
• llama.cpp multi-GPU documentation
• DeepSeek V4 Flash DGX Spark final benchmarks