Qwen3.5 27B needs ~33.9 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~10 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
9.9 tok/s
TTFT
19466 ms
Safe context
395K
Memory
33.9 GB / 108.8 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 9.9 tok/s | 10618 ms | 395K |
| Coding | C | Runs well | 9.9 tok/s | 19466 ms | 395K |
| Agentic Coding | C | Runs well | 9.9 tok/s | 28315 ms | 395K |
| Reasoning | C | Runs well | 9.9 tok/s | 23006 ms | 395K |
| RAG | C | Runs well | 9.9 tok/s | 35393 ms | 395K |
How Qwen3.5 27B (27B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C40 |
Q3_K_S | 3 | 13.2 GB | Low | C40 |
NVFP4 | 4 | 15.1 GB | Medium | C41 |
Q4_K_M | 4 | 16.5 GB | Medium | C41 |
Q5_K_M | 5 | 19.4 GB | High | C41 |
Q6_K | 6 | 22.1 GB | High | C42 |
Q8_0 | 8 | 28.9 GB | Very High | C43 |
F16Best for your GPU | 16 | 55.4 GB | Maximum | C48 |
Copy-paste commands to run Qwen3.5 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-27B-GGUF" \
--hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
NVIDIA H200 141GBGünstige WahlRaises estimated decode speed by about 2373%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 2373%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA B200 180GBNVIDIA-UpgradeRaises estimated decode speed by about 3718%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 9.9 tok/s.
Qwen3.5 27B (27B parameters) requires approximately 33.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Qwen3.5 27B achieves approximately 9.9 tokens per second decode speed with a time-to-first-token of 19466ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 27B on NVIDIA DGX Spark 128GB receives a C grade with 9.9 tok/s and 395K context.
On NVIDIA DGX Spark 128GB, Qwen3.5 27B can safely use up to 395K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-27b-gguf-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: