Short answer: A 24GB GPU can run a 27B model in Q4 quantization when the model weights and context fit. The RTX 4090 is the easiest CUDA upgrade from an RTX 3090, delivering 43.06 tokens per second versus 37.65 on the 3090 in a measured Qwen3.8-27B Q4_K_M run. The RTX 5090 is the faster, roomier option: its 32GB of VRAM and 1,792GB/s bandwidth produced 65.66 tokens per second in the same test. AMD’s Radeon RX 7900 XTX is a viable 24GB alternative if your software stack supports ROCm or Vulkan rather than requiring CUDA.
What a 27B local model actually needs
“27B” describes roughly 27 billion model parameters, not the amount of graphics memory the model always consumes. The file size depends on quantization, while the runtime also needs memory for the key-value (KV) cache, temporary tensors, runtime overhead and your operating system or desktop session.
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For the cited Qwen3.8-27B Q4_K_M build, reported model size is about 18–19GB. That leaves several gigabytes on a 24GB card for runtime overhead and a practical context window, which is why the model was reported to fit fully on a 24GB RTX 3090 and RTX 4090. A higher-quality Q6 or Q8 quantization, a much longer context, batching multiple requests, or another large model loaded at the same time can push the workload beyond that envelope.
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- Q4 quantization: The practical starting point for a single 24GB GPU; the cited 27B model is approximately 18–19GB before runtime allocations.
- Q6 and higher: Better numerical fidelity, but materially less room for KV cache and operating overhead.
- Long context: Memory use rises as the KV cache grows, so a model that loads at a short context may fail at a larger one.
- Full GPU offload: Keeping every layer in VRAM avoids slower CPU/RAM transfers. Partial offload can work, but generation speed generally falls.
Capacity should therefore be checked before speed. A card that is 30 percent faster but still limited to 24GB will not solve a model or context that exceeds 24GB.
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At-a-glance alternatives
| GPU | VRAM | Memory bandwidth | Measured Qwen3.8-27B Q4_K_M speed | Best fit | Main limitation |
|---|---|---|---|---|---|
| RTX 3090 | 24GB | Not stated in the cited test | 37.65 tok/s | Baseline 24GB CUDA system | Less throughput and no extra capacity |
| RTX 4090 | 24GB | Not stated in the cited test | 43.06 tok/s | Faster drop-in CUDA upgrade | Still limited to 24GB |
| RTX 5090 | 32GB | 1,792GB/s | 65.66 tok/s measured; approximately 80 tok/s estimated by another guide | Maximum single-card speed and headroom | 575W-class power and higher platform cost |
| Radeon RX 7900 XTX | 24GB | About 960GB/s | Not stated | AMD/ROCm or Vulkan systems | CUDA-only software may not run |
The throughput figures are specific to Qwen3.8-27B Q4_K_M and should not be treated as universal rates for every 27B model, context length, runtime or driver. The 37.65, 43.06 and 65.66 tok/s figures are 2026 results reported by LLM Speed; the approximately 80 tok/s figure is a 2026 estimate from Llama Builds, not the same kind of measurement.
RTX 4090: the balanced CUDA replacement
What you gain
The RTX 4090 keeps 24GB of VRAM, so it is a capacity-equivalent replacement rather than a solution for models that do not fit on a 24GB card. Its advantage is speed and the mature NVIDIA CUDA software path used by many local-LLM applications.
In LLM Speed’s 2026 Qwen3.8-27B Q4_K_M comparison, the RTX 4090 reached 43.06 tok/s, compared with 37.65 tok/s for the RTX 3090. That is a measured result for that model and test configuration, not a guaranteed uplift for every backend. Your actual rate will vary with context length, prompt processing, GPU clocks, thermal limits, quantization format and whether the runtime keeps all layers on the GPU.
Who should choose it
- Choose the 4090 when you already use CUDA-based tools and want a straightforward, faster single-GPU setup.
- Choose it when your 27B workloads are comfortable within 24GB and you do not need Q6 quantization or unusually long context.
- Do not choose it solely to gain memory capacity; it has the same 24GB ceiling as the 3090.
Practical checks
Confirm that your case supports the card’s physical length and thickness, that the power supply has the required connectors and sufficient continuous capacity, and that your chosen runtime recognizes the current NVIDIA driver. Keep several gigabytes uncommitted rather than setting a context limit that consumes every last megabyte.
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RTX 5090: the speed and headroom choice
Why the extra memory matters
The RTX 5090 provides 32GB of VRAM, eight gigabytes more than a 24GB card. That margin can accommodate a larger KV cache, a higher quantization level, or more runtime overhead without immediately forcing CPU offload. It is still not unlimited: substantially larger models, very long contexts and multi-user batching can exceed 32GB.
Throughput evidence
LLM Speed reports a 65.66 tok/s median measured result for the RTX 5090 on Qwen3.8-27B Q4_K_M in 2026. Llama Builds lists an estimated approximately 80 tok/s for the same model and quantization. Because one number is measured and the other is an estimate, they should not be merged into a single promise. Both indicate a substantial speed advantage over the measured RTX 3090 and RTX 4090 results under their respective methodologies.
Power, cooling and case planning
The 2026 guidance cited for the RTX 5090 specifies 575W board power and recommends at least a 1,000W power supply with strong ventilation. Treat those figures as build requirements, not optional accessories. Before buying, verify:
- Your PSU is a reputable 1,000W-or-higher unit appropriate for the rest of the system and has the connector arrangement required by the card.
- The case provides unobstructed intake and exhaust around a large, high-power board.
- The motherboard slot, card clearance and neighboring-slot spacing are compatible.
- Your room and cooling system can dissipate sustained GPU heat during long generation sessions.
The total cost is therefore more than the graphics-card price: a 5090 build may also require a PSU upgrade, case or fan changes, and adequate electrical and thermal headroom. Prices cited in 2026 buying guides are estimates, not guaranteed offers; check current listings, seller reputation and warranty terms before purchase.
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Who should choose it
Choose the 5090 when you value the highest single-card throughput, need 32GB for larger contexts or Q6-style workloads, or want room for future models. It is difficult to justify if your only requirement is a Q4 27B model that already runs comfortably on 24GB and your priority is minimizing purchase and operating cost.
Radeon RX 7900 XTX: the AMD path
Hardware fit
The RX 7900 XTX has 24GB of VRAM and approximately 960GB/s of memory bandwidth in the cited 2026 specification summary. That capacity is suitable for the reported 18–19GB Qwen3.8-27B Q4_K_M model with room for a practical context, provided the runtime and operating system reserve enough memory.
Software decision
The central trade-off is not capacity but software compatibility. NVIDIA-focused applications and extensions that require CUDA may need an AMD-specific backend, a Vulkan path, or may not run at all. ROCm support can be effective when your operating system, driver and framework combination is supported, but the setup is less universally plug-and-play than the common CUDA path.
For Ollama or llama.cpp, verify the backend documented for your exact operating system and release before buying. A model file fitting in VRAM does not guarantee that a particular binary will use the GPU. Check that the application detects the card, that the selected backend is actually active, and that layers are offloaded rather than silently running on the CPU.
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Who should choose it
- Choose the 7900 XTX when you already maintain an AMD/ROCm or Vulkan workflow and 24GB is enough for your target models.
- Choose it when the card’s price or availability is substantially better than an equivalent NVIDIA option and you accept backend-specific setup.
- Avoid it for a CUDA-only toolchain unless you have confirmed a supported alternative backend for every application you need.
How to choose by workload
| Your requirement | Most suitable choice | Reason |
|---|---|---|
| Q4 27B, standard context, existing CUDA setup | RTX 4090 | Higher measured throughput than the 3090 without changing the software ecosystem. |
| Q4 27B plus longer context or simultaneous runtime overhead | RTX 5090 | 32GB leaves more allocation headroom. |
| Q6 quantization or future 30B-class experimentation | RTX 5090 | Additional VRAM reduces the chance of CPU offload. |
| AMD workstation with ROCm/Vulkan-compatible applications | RX 7900 XTX | 24GB capacity and about 960GB/s bandwidth can support a single-card Q4 workload. |
| Models or contexts larger than one 24–32GB card | Evaluate multi-GPU or unified-memory/workstation systems separately | The cited single-card figures do not establish a universal multi-GPU solution. |
Installation and validation checklist
- Estimate memory before purchasing. Add the quantized model file, expected KV-cache allocation, runtime overhead and a safety margin. Use the reported 18–19GB Qwen3.8-27B Q4_K_M size as a reference, not as a guarantee for other 27B files.
- Select the backend. NVIDIA systems generally use CUDA; AMD systems require a supported ROCm or Vulkan route. Confirm support for your exact application and operating-system version.
- Install the current driver and runtime. Reboot if required, then verify that the operating system reports the GPU and the application lists the intended accelerator.
- Load the model with conservative settings. Start with a moderate context length and full GPU offload. Increase context only while monitoring VRAM use and generation stability.
- Measure prompt and generation performance separately. Record tokens per second, context length, quantization, driver, backend and thermal state. This makes comparisons meaningful and explains why your result may differ from published figures.
- Stress-test the real workload. Run your longest expected prompt, largest context and normal concurrency. Stop before VRAM exhaustion rather than relying on automatic CPU spillover.
Common failure modes and fixes
The model loads but generation is unexpectedly slow
Check whether all layers were placed on the GPU. A model can technically start while part of it remains in system RAM. Confirm the backend, inspect reported offload counts, reduce context or use a smaller quantization, and watch for thermal or power throttling.
The application reports out-of-memory errors
Reduce context length first, then lower batch size or choose a smaller quantization. Close other GPU applications and leave a safety margin. Moving from a 24GB card to another 24GB card will not remove a capacity limit; the RTX 5090’s 32GB is the relevant single-card upgrade.
An AMD card is detected but not used
Verify that the installed build includes the ROCm or Vulkan backend you selected. Confirm driver compatibility, select the backend explicitly when the application allows it, and test with a small model before loading the 27B file. If the tool requires CUDA with no alternative backend, the RX 7900 XTX is not a compatible choice for that tool.
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The system crashes during long sessions
For a high-power card, inspect PSU capacity, connector seating, case airflow and GPU temperatures. The RTX 5090 guidance calls for a 1,000W PSU and strong ventilation because its specified board power is 575W.
Best Value
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Multi-GPU and larger-memory systems
If your target model or context exceeds 32GB, consider multiple GPUs or a unified-memory/workstation platform rather than assuming a second card will behave like one larger card. Splitting weights across GPUs introduces PCIe transfers, placement configuration and inter-card bandwidth limits. The cited results establish fit and speed for the individual cards above, but they do not provide a controlled benchmark covering every multi-GPU arrangement.
For a multi-card plan, budget for motherboard slot spacing, PSU capacity, cooling and software support in addition to the GPUs. Validate that your runtime can split layers as intended and decide whether the resulting latency is acceptable for interactive use.
Cost and buying precautions
Graphics-card prices change by region, stock and date. The October 2026 figures in the buying guides are estimates rather than guaranteed offers, so compare current seller prices, return policies and warranty coverage. Include the cost of a suitable PSU, case airflow upgrades and any platform changes in the total rather than comparing card prices alone.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →For most buyers, the decision is straightforward: select the RTX 4090 for a familiar CUDA stack and a measured speed increase while remaining within 24GB; select the RTX 5090 when 32GB and higher throughput justify its power and platform demands; select the RX 7900 XTX when AMD software support is confirmed and 24GB is sufficient.
Quick Recap
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