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Yes. A 24 GB RTX 3090 can run some 27B models locally when the weights are quantized and runtime memory is kept under control. It is a close fit, not a guarantee: weights share VRAM with the KV cache, runtime buffers, optional model components, the display, and other applications. One documented Qwen3.8-27B setup fit all model layers on a single RTX 3090 and peaked at 22,162 MiB, but that result applies to its specific settings, not every model or computer.
How much VRAM does a 27B model need?
The RTX 3090 has 24 GB of GDDR6X memory, according to NVIDIA’s product specifications. That is the card’s capacity, not the amount guaranteed to be available for inference: the operating system, display, other GPU processes, and the inference runtime may use some of it.
“27B” describes a model’s approximate parameter count; it does not specify the GPU memory required to run it. Quantization changes how much memory the weights occupy, while the KV cache grows with active context. Runtime buffers and optional components require additional memory. The actual fit therefore depends on the model file, its quantization, the runtime and cache settings, the context in use, and available VRAM.
A specific Qwen3.8-27B report used Q4_K_M weights, a q8_0 KV cache, a configured 131,072-token context, and all layers on one RTX 3090. It recorded peak GPU use of 22,162 MiB. That demonstrates a single-card fit with those settings, but the peak leaves limited room for other allocations. The individual field report is not a guarantee for another model file or system.
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Model files and optional components matter
A separate technical guide reports a 14.25 GB (13.3 GiB) UD-IQ4_XS Qwen3.8-27B weight file. In its tested setup, an optional BF16 vision projector used 1,138 MiB of resident VRAM. These are measurements for that particular release and configuration; they should not be treated as standard sizes for every 27B model or format. The guide also shows that runtime features and the memory reserve change the context that can fit. See the Qwen3.8 configuration guide.
What tokens per second can you expect?
There is no single reliable speed figure for “a 27B model on an RTX 3090.” Decode speed, prompt-processing speed, time to first token, and end-to-end response time measure different parts of inference. Context length, quantization, cache precision, reasoning-token output, backend, and prompt all affect the result.
For a concrete reference, one 2026 Qwen3.8-27B field report measured 36.4 tokens per second on a 2,073-token input with thinking disabled. The run used llama.cpp, Q4_K_M weights, a q8_0 KV cache, flash attention, one generation slot, and all layers on a single RTX 3090; peak GPU memory was 22,162 MiB. At 120K context, the same report recorded 20.9 tokens per second. These are setup-specific field measurements, not guaranteed performance. Read the report and its configuration details.
Another technical guide reports different Q4_K_M results with a built-in speculative decoding head: 57.9 tokens per second on a reasoning stream and 69.8 on answer tokens under its stated configuration. It also describes 81.7 tokens per second as answer-token performance on a deliberately novel code prompt. Those figures are not directly comparable with the field report above because the software build, prompt, decoding setup, and token type differ. The guide describes its measurement setup.
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How much context can a 3090 handle?
A model’s configured or advertised context limit is not the same as the context a particular runtime can practically use on a 24 GB card. The KV cache consumes additional memory as active context grows, and the weight allocation already takes a substantial share of VRAM. A context limit printed in a model or runtime configuration does not by itself mean that the full window will fit in GPU memory or run at the same speed throughout.
In the cited single-card report, throughput fell from 36.4 tokens per second on a short input to 20.9 tokens per second at 120K context. The configuration included q8_0 KV cache and Q4_K_M weights; that measured result is a useful example of the context-speed trade-off, not a universal performance curve. The Qwen3.8 guide likewise distinguishes configured windows from practical limits under its tested memory settings. Consult the guide for those setup-specific measurements.
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There is no defensible universal maximum context for every 27B model on an RTX 3090. Practical capacity changes with weight quantization, KV-cache precision, available-memory reserve, backend, optional projectors or draft models, and how much context is actually filled.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which settings should you compare?
| Approach | What it prioritizes | Trade-off to evaluate |
|---|---|---|
| Smaller weight quant or more memory-efficient KV cache | Potentially more room for context or other allocations | Quality and speed effects depend on the particular model and backend; the cited sources do not establish one universally best choice. Configuration guide |
| Q4_K_M weights with q8_0 KV cache and a managed context | A concrete single-card reference configuration | One report peaked at 22,162 MiB, so available memory margin matters; its measured speed was lower at 120K context. Field report |
Compare configurations using the same model and workload where possible. Focus on usable context, memory margin, output quality for the model you intend to run, and decode speed at your expected prompt length. Do not treat tokens-per-second figures from different prompts, builds, or token regimes as controlled head-to-head benchmarks. Community reports and aggregated user records are useful as setup-specific evidence, not universal predictions. llamaperf’s records aggregate user reports rather than controlled results.
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