DeepSeek V4 vs Qwen3.8 DGX Spark

Quick verdict

Quick verdict guidance: Choose Qwen3.8-27B for a fast, practical local assistant; choose DeepSeek V4 Flash only when its much larger model quality justifies specialized low-bit hardware and lower operational simplicity.

Comparison table

Memory / capacity

Memory / capacity guidance: Qwen3.8 fits in roughly 16–19 GB at 4 bit. DeepSeek V4 Flash has 284B total parameters and needs around 142 GB even at an idealized 4 bits before overhead, so one consumer GPU is not comparable.

Observed LLM performance

Observed LLM performance guidance: Qwen can deliver tens to low hundreds of tok/s on one modern GPU. DeepSeek may produce low-double-digit single-node output or higher clustered throughput, but raw speed is not its buying reason.

Prefill vs decode

Prefill vs decode guidance: Both advertise long context, but DeepSeek's weight footprint leaves less practical room. Start Qwen at 16K and DeepSeek at 8K–16K until memory is characterized.

Power

Power guidance: Smaller Qwen deployments generally use less total energy per answer. DeepSeek's low active parameter count helps compute efficiency but not resident weight capacity.

Current market cost

Current market cost guidance: Qwen runs on a single consumer card; DeepSeek can require several times the hardware, storage, download, and operator effort.

Which models fit

Which models fit guidance: Qwen3.8 fits in roughly 16–19 GB at 4 bit. DeepSeek V4 Flash has 284B total parameters and needs around 142 GB even at an idealized 4 bits before overhead, so one consumer GPU is not comparable.

Who each option suits

Who each option suits guidance: Choose Qwen for personal assistants, coding, RAG, and small services. Choose DeepSeek for research or high-value tasks where capability outweighs hardware and operations.

What stands out

Deploy Qwen by default. Escalate to DeepSeek only after Qwen fails a task evaluation and the quality gain justifies at least 128–192 GB of accelerator memory.

Qwen can deliver tens to low hundreds of tok/s on one modern GPU. DeepSeek may produce low-double-digit single-node output or higher clustered throughput, but raw speed is not its buying reason.

Qwen3.6-35B-A3B offers a stronger agentic middle ground; Mistral Small 4 and gpt-oss-120b provide large-model options that fit more comfortably on 128 GB.

Evidence limitations

• Active parameters measure per-token compute, not total stored experts.
• Do not justify a large deployment with benchmark rankings that do not match the intended workload.

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.

• Qwen3.8-27B model repository
• DeepSeek-V4-Flash model card
• vLLM installation and hardware documentation
• llama.cpp project and backend documentation
• DeepSeek V4 Flash DGX Spark final benchmarks
• StackBench controlled lab approved source qwen38-dgx-spark-owner-archive-20260817