Can the NVIDIA DGX Spark run Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next · NVIDIA DGX Spark · 8,192 ctx · whole model residentQ5_0 sizesize rangeSpeedestimate

IT FITS

Yes. 5 of 9 formats evaluated fit in 128 GB, at 8,192 tokens. No published file exists for this pair, so every size is rebuilt from the pinned architecture. The best quality that fits is Q5_0, needing 126.7 GB and running at an estimated 46 tokens per second.

Fitting in memory is not the same as loading. Whether the runtime and version you have supports this architecture and format on this machine has not been tested here.

Find the NVIDIA DGX Spark: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

5/9formats that fit

128 GBdevice memory

126.7 GBneeded at best quality

~46tokens per second, estimatedFaster than you read

273GB/s bandwidth

How long a conversation. At Q5_0, this pair holds 59,544 tokens of context — roughly 44,658 words — needing 128.0 GB. Past that the NVIDIA DGX Spark runs out of memory, not the model out of context, which would allow 262,144. This is the wall a long chat hits after it has already loaded fine. The nearest round setting below it is 32,768.

Every format evaluated

No published file exists for this pair, so every size is rebuilt from the pinned architecture. The Basis column says which is which for each row. Decode and first-token figures are calibrated estimates, not runs on this card.

Format	Needs	Of 128 GB	Fits	Decode	First token	Basis
FP16	361.3 GB	282%	short by 233.3 GB	does not run	—	size range
Q8_0	192.5 GB	150%	short by 64.5 GB	does not run	—	size range
Q6_K	148.9 GB	116%	short by 20.9 GB	does not run	—	size range
Q5_K_M	129.7 GB	101%	short by 1.71 GB	does not run	—	size range
Q5_0	126.7 GB	99%	yes	~46 tok/s	no comparable peak	size range
Q4_K_M	111.6 GB	87%	yes	~52 tok/s	no comparable peak	size range
Q4_0	105.9 GB	83%	yes	~53 tok/s	no comparable peak	size range
Q3_K_M	91.1 GB	71%	yes	~60 tok/s	no comparable peak	size range
Q2_K	72.4 GB	57%	yes	~72 tok/s	no comparable peak	size range

Sized at 8,192 tokens of context with the whole model resident — weights, the KV cache and the runtime reserve, offload off. Speed is only quoted for a format that fits: a rate for a configuration that cannot load is not a fact about anything.

Why the first-token figure is the same on every row

First token is modelled from the arithmetic the prompt requires, and that count does not change with the weight format — which is why it reads the same on every row. Real prefill does vary by format, because a quantized matmul is a different kernel; this model does not capture that, and the figure should be read as an order of magnitude rather than a ranking between formats.

What the labels mean

published data: Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size: Weight size reconstructed from the pinned architecture, because no published file exists.
size range: Only a lower and an upper bound are claimed for this weight size.
estimate: Calculated from sourced inputs by a stated method; an estimate, not a measurement.

Change anything

This page fixes the context at 8,192 tokens and one device. Batch, concurrent users, KV-cache format, clusters and rental cost are all in the calculator, already set to this pairing.

Open in the calculator →Everything the NVIDIA DGX Spark runs Best models for 128 GB

Where these numbers come from

Qwen3.8-Flash-Next’s architecture is read from its publisher’s own config.json at a pinned revision, and the NVIDIA DGX Spark’s 128 GB and 273 GB/s come from the manufacturer’s specification. How far each figure can be trusted is published, per format and worst case included, on the accuracy scorecard.

Building this into your own product? A free API key returns exactly these figures, and the widget puts this answer on a product page with one script tag.

Use this answer in your own product

As JSON, from the API

curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"qwen-qwen3-8-flash-next","quantization":"Q5_0","context":8192,"hardware":"nvidia-dgx-spark"}'

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On your page, as a widget

<script src="https://llmbottleneck.com/widget.js"
  data-model="qwen-qwen3-8-flash-next" data-quantization="Q5_0"
  data-hardware="nvidia-dgx-spark" data-context="8192"></script>

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