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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDeepSeek V4.1-Flash has been reported at 494 tokens per second for code generation on four NVIDIA DGX Spark systems—but that is aggregate throughput across 32 concurrent requests, not the speed of one chat response. The same report lists about 96 tokens per second for a single code request and about 58 for single-request prose. Those figures are attributed to a setup disclosure and have not been independently reproduced in the sources available.
What does the 494 tokens-per-second figure mean?
Wccftech reported the 494 tokens-per-second code result on October 5, 2026, attributing the setup and figures to Patrick Moorhead’s social post. The number is for 32 requests running concurrently, so it represents cluster-wide output across the workload. It does not mean that an individual user sees one response stream generating 494 tokens every second. Wccftech’s report does not establish an independently reproduced benchmark protocol for that specific result.
The same article reports 280 tokens per second for prose at 32 concurrent requests, approximately 96 tokens per second for a single code request, and approximately 58 for single-request prose. It also lists approximately 4,764 tokens per second for prompt processing and about 0.2 seconds to first token while idle. These are reported values tied to that setup, not universal performance guarantees; prompt processing and generated output are different phases, and the idle first-token figure is not a measure of sustained generation speed.
How do other four-Spark results compare?
A separate benchmark post by the repository maintainer describes a tuned four-Spark setup using vLLM, tensor parallelism, DSpark speculative decoding, and CUDA graphs. It reports 77.2 tokens per second peak on a single counting stream, 52 tokens per second on code in the benchmark, 72 on a warm code run, and 214 tokens per second aggregate at six streams. The post also reports 143 aggregate tokens per second on code at six streams. These values come from a different workload and configuration and do not verify the 494 result. The forum post is an individual benchmark account, not a standardized independent test.
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The repository’s September 10, 2026 benchmark notes provide another illustration of workload sensitivity. In that run, one-stream decoding measured 73.8 tokens per second on code, 50.9 on math, 37.8 on reasoning, and 24.4 on prose. At six streams, the aggregate across eight prompt categories was 131.9 tokens per second; the peak aggregate for code at six streams was 225.5. The author noted that a GPU slow-state condition affected one run. The repository notes describe their own setup and should be read as contextual results, not a replication of Moorhead’s reported setup.
| Reported setup and source | Workload and concurrency | Reported output rate | How to interpret it |
|---|---|---|---|
| Four-Spark setup reported by Wccftech, October 5, 2026 | Code, 32 concurrent requests | 494 tokens/second | Attributed aggregate result; not independently reproduced in the cited material. |
| Same Wccftech report | Prose, 32 concurrent requests | 280 tokens/second | Aggregate across concurrent requests. |
| Same Wccftech report | One code request | Approximately 96 tokens/second | Single-request output rate as reported. |
| Same Wccftech report | One prose request | Approximately 58 tokens/second | Single-request output rate as reported. |
| Maintainer’s separate tuned four-Spark vLLM setup | Counting, one stream | 77.2 tokens/second peak | Specific task and tuned configuration; not comparable as a universal model speed. |
| Maintainer’s separate tuned four-Spark vLLM setup | Code, six streams | 143 tokens/second aggregate | Different concurrency and benchmark configuration from the 494 report. |
| Repository notes, September 10, 2026 | Code, one stream | 73.8 tokens/second | Repository benchmark result; author noted a GPU slow-state condition affected one run. |
| Repository notes, September 10, 2026 | All eight prompt categories, six streams | 131.9 tokens/second aggregate | Aggregate across that run’s categories; not a single-stream rate. |
What kind of model is DeepSeek V4.1-Flash?
DeepSeek’s September 2026 paper describes V4.1-Flash as a multimodal mixture-of-experts model with 552 billion backbone parameters and support for context lengths up to one million tokens. The paper says 8 billion parameters are active per token during prefill and 16 billion during decode. The total parameter count therefore should not be read as the number of parameters used for every token in every phase.
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The authors report a global KV-cache footprint of 890 bytes per token, roughly one quarter of the corresponding DeepSeek-V4-Flash footprint. They attribute the reduction to cross-layer KV reuse in Compressed Sparse Attention 2 and FP4 KV caching, and describe SWA Bounded Replay as reducing persistent KV-cache requirements. These are architecture claims in the model paper, not independent measurements of memory use on the four-Spark benchmark. Read the DeepSeek authors’ paper.
What hardware and software does a four-Spark setup involve?
NVIDIA lists each DGX Spark with a 20-core Arm CPU, up to 128 GB of coherent unified memory, 273 GB/s memory bandwidth, and a ConnectX-7 network interface rated at 200 Gbps. Four systems offer a nominal total of up to 512 GB of system memory across the cluster, but that pooled figure does not turn them into one ordinary workstation with a single shared memory address space. The reported benchmark is a multi-system serving deployment, and its performance depends on how the model and workload are distributed across nodes.
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The separate forum configuration describes four DGX Sparks connected for tensor parallel serving, with vLLM, DSpark speculative decoding, and CUDA graphs. Its author also says 203 GB of Engram tables remain on disk. NVIDIA’s listed specifications describe individual systems; they do not by themselves establish benchmark throughput or guarantee that any four-unit setup will reproduce the reported rates. NVIDIA’s DGX Spark specifications.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare DeepSeek benchmark speeds?
Before treating one tokens-per-second result as faster than another, check that the compared runs match on the factors that materially change what the number means:
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- Single stream or aggregate: a per-request rate and total cluster throughput answer different questions.
- Concurrency: 32 simultaneous requests can produce a high combined rate even when each request proceeds much more slowly.
- Task and prompt category: code, prose, math, reasoning, and counting have different reported rates.
- Serving configuration: software, quantization, speculative decoding, and graph settings can affect results.
- Prompt and context workload: context length and prefill volume affect the work done before output decoding.
- Evidence quality: distinguish a secondary report of a social post, an individual benchmark account, and a public run with documented configuration; none should be silently substituted for another.
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