You can run Qwen3.8-27B with one GPU, but whether it runs entirely in GPU memory—and how fast it responds—depends on the checkpoint, usable memory, context length, runtime and workload. If the weights do not fit, CPU offloading can make inference possible by keeping some model layers in system RAM, but the CPU portion can slow generation. There is no single VRAM minimum or reliable tok/s figure that applies to every setup.
What changes when weights move between GPU and CPU?
Qwen3.8-27B is a dense, 27-billion-parameter causal language model with a vision encoder. Qwen lists 64 layers and a hybrid architecture alternating three Gated DeltaNet blocks with one gated-attention block. Its model card specifies a native 262,144-token context, with extension up to 1,000,000 tokens. Those are model capabilities, not a promise that a consumer GPU can hold the model and run every workload at those context lengths. Qwen model card
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In full GPU residency, the model’s weights are held in GPU memory while inference runs. With CPU offloading, some weights or layers are placed in system RAM and processed through a hybrid CPU/GPU path. That can turn an otherwise-too-large checkpoint into a runnable one, but it does not make the CPU-resident portion as fast as work performed in GPU memory. The resulting speed depends on the particular CPU, memory bandwidth, GPU, transfer path, runtime and layer placement.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesWeights are only part of the memory budget. The runtime also needs room for the KV cache and other allocations; context length, cache precision, batch or concurrency, and image or video inputs can increase demand. Usable GPU memory is also less than its advertised capacity when the display, runtime or other processes occupy some of it. So a checkpoint that fits by weight-file size alone may still fail to load or run at the desired context and workload.
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How large are the weights, and what does quantization change?
Weight size varies substantially with precision and quantization. The figures below come from different sources and describe particular artifacts or configurations; they are not a universal sizing guide. The 8 GB values are reported by a third-party benchmark project, while the BF16 and FP8 figures are from a separate DGX Spark report.
| Format or configuration | Reported weight size | Source and qualification |
|---|---|---|
| BF16 | 54.7 GB | Artifact size listed by the GitHub benchmark project for its 8 GB RTX 5070 Laptop test. |
| FP8/INT8 | 29.0 GB | Artifact size listed by the GitHub benchmark project for its 8 GB RTX 5070 Laptop test. |
| NVFP4/AWQ int4 | About 14 GB | Approximate artifact size listed by the GitHub benchmark project for its 8 GB RTX 5070 Laptop test. |
| Q4_K_M | 17.1 GB | Artifact size listed by the GitHub benchmark project for its 8 GB RTX 5070 Laptop test. |
| Q3_K_S | 12.6 GB | Artifact size listed by the GitHub benchmark project for its 8 GB RTX 5070 Laptop test. |
| IQ2_XXS | 9.0 GB | Artifact size listed by the GitHub benchmark project for its 8 GB RTX 5070 Laptop test. |
| BF16 | 55.6 GB | Weight size reported for the one-device DGX Spark configuration in the 2026 forum study. |
| FP8 | 30.9 GB | Weight size reported for the one-device DGX Spark configuration in the 2026 forum study. |
The official Qwen FP8 checkpoint uses fine-grained FP8 quantization with a block size of 128. Qwen says its performance metrics are “nearly identical” to the original model’s metrics; that is the vendor’s statement about its reported metrics, not a guarantee of equal quality, speed or memory fit on every GPU and runtime. Qwen FP8 model card
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Third-party quantized checkpoints can differ in quantization method, file size, quality and runtime support. Qwen’s model card lists serving instructions for Transformers, vLLM and SGLang, and points readers to quantized variants for llama.cpp, Ollama and LM Studio. Check the exact checkpoint and runtime together rather than treating all files labeled “4-bit” or “FP8” as interchangeable. Qwen model card
What does CPU offloading look like on an 8 GB GPU?
A 2026 GitHub benchmark project tested an RTX 5070 Laptop with 8,151 MiB of VRAM, an Intel i7-14650HX and 30 GB of DDR5 RAM. The author reported about 7.3 GB of usable VRAM. None of the listed model files—from 9.0 GB for IQ2_XXS to 54.7 GB for BF16—fit entirely in that usable capacity, so the reported setup used a hybrid placement strategy. These artifact sizes and results belong to the project, not to Qwen’s official sizing guidance. GitHub benchmark project
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For an empty-context llama.cpp test, the project reported the following decode throughput as more layers were placed on the GPU:
| Layers on GPU | Reported throughput |
|---|---|
| 20 | 5.28 tok/s |
| 30 | 6.05 tok/s |
| 40 | 7.61 tok/s |
| 46 | 9.30 tok/s |
| 50 | 10.78 tok/s |
| 54 | 12.87 tok/s |
| 56 | 15.82 tok/s, described by the project as the ceiling for this test |
| 58 | Out of memory |
In the same machine’s measurements, the project reported 353.0 GB/s GPU VRAM read bandwidth, 43.9 GB/s CPU DRAM bandwidth and 18.2 GB/s PCIe host-to-device bandwidth. Its explanation is that the CPU-resident fraction becomes a bottleneck. The table illustrates how layer placement affected one particular test; it is not a speed range or prediction for other systems. The result depends on that laptop, its 30 GB RAM, model file and quantization, llama.cpp settings, and the empty-context protocol. GitHub benchmark project
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What do one-GPU results show?
A separate report posted to the NVIDIA Developer Forums on August 24, 2026, tested Qwen3.8-27B on one DGX Spark. The report describes a GB10 Grace Blackwell system with 128 GB of unified memory and 273 GB/s LPDDR5X bandwidth. For its concurrency-one runs using official vLLM, it reported:
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|---|---|---|---|
| BF16 | 55.6 GB | 4.5 tok/s | 335 ms |
| FP8 | 30.9 GB | 7.9 tok/s | 172 ms |
The same post calculated bandwidth-only ceilings of approximately 4.9 tok/s for BF16 and 8.8 tok/s for FP8 using its stated bandwidth and model sizes. These are the post’s calculations, not measured throughput. It also reported 9.9 tok/s for BF16 at concurrency one with three speculative tokens, and 18.5 tok/s for one NVFP4 configuration with multi-token prediction (MTP). Those figures use different precision or decoding settings and do not isolate the effect of offloading. NVIDIA Developer Forums report
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Other community reports are not controlled comparisons. One individual report describes a single RTX 4090 24 GB setup using full GPU offload at a stated 160K context and 47–57 tok/s; it is not independently reproduced evidence that every 24 GB card will achieve that speed or context. A separate RTX 4070 Ti SUPER 16 GB optimization whitepaper uses an EXL3 3.0 bpw checkpoint and a customized ExLlamaV3 fork, including pinned host RAM for vision data and quantized KV cache for its stated context targets. Its techniques and results are configuration-specific. RTX 4090 community report · RTX 4070 Ti SUPER optimization whitepaper
These reports use different systems, runtimes, precision, context and test protocols. They cannot establish a fair speed ranking between CPU offloading and full GPU residency. No reviewed report holds those variables constant while changing only whether weights are offloaded across multiple hardware tiers.
How to choose a setup for your workload
Start with the exact checkpoint and the job you want to run, then estimate whether the usable memory can accommodate more than just the weights. A practical comparison should include:
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- Checkpoint and weight memory: identify the exact precision or quantization and its documented or measured size.
- Memory available to inference: account for VRAM or unified memory consumed by the display, runtime, vision processing and other applications.
- CPU resources and placement: check system RAM capacity and bandwidth, and determine which layers or tensors the runtime places on the CPU and how data moves between CPU and GPU.
- Context and cache: use the intended prompt length and cache precision. Empty-context results do not describe a populated long-context run.
- Runtime and kernels: confirm that the chosen checkpoint works with your serving software and note the relevant version and settings.
- Workload and metric: distinguish prompt processing and time to first token from decode tok/s; record output length, batch or concurrency, vision/video input and speculative decoding.
- Quality: evaluate the exact quantization on the tasks that matter to you. Model-card quality metrics do not predict local inference speed.
If the weights fit but the cache or workload does not, reducing context, concurrency or other allocations may be more useful than simply comparing the checkpoint’s file size with the GPU’s capacity. If the weights do not fit, a supported hybrid CPU/GPU configuration may still work; expect its CPU-resident portion and memory-transfer path to influence decode speed. Test with the runtime and context you actually plan to use.
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