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Short answer: choose the GeForce RTX 4090 if you want a familiar CUDA setup with a measurable speed increase, the GeForce RTX 5090 if you need the most throughput and memory headroom, or the Radeon RX 7900 XTX if 24GB is sufficient and your software supports ROCm or Vulkan. A 24GB card can run a dense 27B model such as Qwen3.8-27B in Q4_K_M quantization fully on the GPU, provided the runtime and context fit in memory.
The RTX 4090 is a capacity-equivalent replacement for an RTX 3090, not a solution for models that exceed 24GB. The RTX 5090 adds 32GB of VRAM and much higher bandwidth, making higher quantization levels and longer contexts more practical. The RX 7900 XTX offers 24GB at a potentially lower platform cost, but CUDA-only software and less mature AMD support can make setup less predictable.
Which RTX 3090 replacement should you buy?
Your decision comes down to memory capacity first, then software compatibility, speed, and the cost of the supporting hardware.
| GPU | VRAM | Memory bandwidth | Measured Qwen3.8-27B Q4_K_M throughput | Best fit |
|---|---|---|---|---|
| GeForce RTX 4090 | 24GB | Not stated in the cited test summary | 43.06 tokens/second (LLM Speed, 2026) | CUDA users who want a straightforward speed upgrade without changing capacity |
| GeForce RTX 5090 | 32GB | 1,792GB/s | 65.66 tokens/second median measured result (LLM Speed, 2026) | Higher throughput, Q6 quantization, or longer context windows |
| Radeon RX 7900 XTX | 24GB | About 960GB/s | Not stated in the cited test summary | AMD systems where ROCm or Vulkan support is acceptable |
| GeForce RTX 3090 | 24GB | Not stated in the cited test summary | 37.65 tokens/second (LLM Speed, 2026) | Baseline for comparison |
For the same Qwen3.8-27B Q4_K_M workload, the measured results put the RTX 4090 ahead of the RTX 3090 and the RTX 5090 substantially faster than both. Those numbers are tied to that model, quantization, runtime, and test method; they are not a guarantee for every 27B model or application.
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Is 24GB enough for a 27B language model?
Often, yes. Qwen3.8-27B in Q4_K_M is reported at approximately 18–19GB, leaving several gigabytes on a 24GB card for runtime allocations and a practical context window. That is why both the RTX 3090 and RTX 4090 can place every layer on the GPU in the cited run.
Model-file size is not the same as total VRAM use. The runtime also needs memory for the key-value (KV) cache, temporary compute buffers, CUDA or ROCm allocations, and the operating system’s graphics overhead. The KV cache grows as the context gets longer, so a configuration that fits at a short prompt can run out of memory during a long conversation or document task.
When 24GB becomes marginal
- Higher-precision quantization: Q5, Q6, or Q8 versions retain more information but require more memory than Q4_K_M. A Q6 build may fit on 24GB for some models and fail for others, depending on the exact tensor layout and runtime overhead.
- Long context: extending the context window increases KV-cache use. The available headroom after loading the weights determines how far you can extend it.
- Different 27B architectures: “27B” describes parameter count, not an identical file size. Mixture-of-experts models, multimodal variants, and different quantizers can have very different memory requirements.
- Shared display use: a card driving monitors or running other GPU applications has less memory available to the language-model process.
Check the exact quantized file size and the runtime’s reported allocation before selecting a context limit. If the model consumes close to the card’s capacity at load time, leave the context conservative rather than assuming the remaining memory is all usable.
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The RTX 4090 keeps 24GB of VRAM, so it does not expand the class of models that fit entirely on one card. Its advantage is speed and a mature CUDA software path. In the cited LLM Speed 2026 result, it produced 43.06 tokens per second on Qwen3.8-27B Q4_K_M, compared with 37.65 tokens per second for the RTX 3090 under that test.
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Why choose the 4090
- Minimal software change: CUDA-oriented runtimes and libraries used with the RTX 3090 generally have a familiar migration path.
- Higher generation speed: the cited result is about 14% faster than the RTX 3090 result for that workload.
- Same capacity envelope: existing 24GB model files and memory planning remain broadly comparable.
- Useful for interactive use: faster token generation reduces wait time for chat, coding, and iterative prompting when the model already fits in 24GB.
What the 4090 does not solve
It will not make a model that needs more than 24GB fit on one card. It also does not provide the memory margin needed for every Q6 model or very long context. If your current limitation is an out-of-memory error rather than slow generation, moving from a 3090 to a 4090 may leave the underlying problem unchanged.
GeForce RTX 5090: the headroom and throughput choice
The RTX 5090 is the strongest single-card option in the supplied comparisons. Llama Builds lists 32GB of VRAM and 1,792GB/s of memory bandwidth, while LLM Speed reports a median measured 65.66 tokens per second on Qwen3.8-27B Q4_K_M in 2026. Llama Builds also lists an estimated result of approximately 80 tokens per second; treat that figure as an estimate rather than a directly comparable measurement.
What the extra 8GB enables
- More quantization choices: a 32GB card gives you room to test Q5 or Q6 variants that would leave too little headroom on 24GB.
- Longer contexts: additional free VRAM can be assigned to the KV cache after the model is loaded.
- Less memory pressure: runtime buffers and desktop usage are less likely to force partial CPU offload.
- More room for future models: a 32GB limit remains restrictive for some larger models, but it postpones the point at which multi-GPU or unified-memory hardware becomes necessary.
Power, cooling, and case planning
The RTX 5090 guidance cited for this comparison specifies 575W board power and recommends at least a 1,000W power supply with strong ventilation. Those figures make the power system part of the purchase decision, not an afterthought.
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- Confirm that the power supply has the required high-current GPU connector and enough capacity for your CPU, drives, fans, and transient load.
- Check card length, thickness, and slot clearance against the case and motherboard. A large card can block adjacent expansion slots.
- Use unobstructed intake and exhaust paths. Sustained inference loads can keep the GPU busy for long periods, unlike short gaming bursts.
- Update the graphics driver and verify stable temperatures under a representative generation workload before relying on the system for unattended jobs.
A 5090 purchase can therefore require a new power supply or case airflow changes. Include those costs when comparing it with a 4090 or a 7900 XTX.
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Radeon RX 7900 XTX: a 24GB AMD alternative
The RX 7900 XTX provides 24GB of VRAM and approximately 960GB/s of memory bandwidth in the Llama Builds specification summary. That capacity is sufficient for the cited Q4_K_M 27B fit when the runtime and context stay within the limit.
Where it fits well
- You already use an AMD platform and can run a ROCm-enabled or Vulkan-enabled inference stack.
- Your model, quantization, and context target fit in 24GB.
- You are willing to select software by backend rather than assuming every CUDA tutorial applies unchanged.
Compatibility limits
CUDA-only tools will not run natively on the RX 7900 XTX. Some applications offer ROCm builds, some use Vulkan, and others support only NVIDIA CUDA for particular acceleration paths. Ollama and llama.cpp support depends on the release, operating system, and backend configuration you install; verify that your exact version exposes a working ROCm or Vulkan path before buying the card for a specific workflow.
Expect more setup variation than with an NVIDIA card: driver versions, kernel support, runtime libraries, and backend flags can all affect whether the GPU is detected and whether all layers remain on the device. If a project’s documentation lists only CUDA, the 7900 XTX is not a drop-in replacement.
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The graphics API and acceleration backend determine whether the model runs on the GPU, how easily it installs, and which optimizations are available.
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CUDA on NVIDIA
CUDA is the most direct path for the RTX 4090 and RTX 5090. If your existing 3090 setup already uses a CUDA build of a local inference engine, migration normally involves installing the current driver, selecting the same model format, and checking that the new device is detected. You still need to validate memory use because a faster GPU does not automatically increase the context limit.
ROCm on AMD
ROCm is AMD’s compute software path. A compatible ROCm build can use the 7900 XTX, but support varies by operating-system release and application. Confirm the project’s supported GPU list and installation instructions for your exact environment; do not assume that a generic Linux or Windows package includes ROCm acceleration.
Vulkan as a portability option
Vulkan backends can provide a more portable route for projects that implement them, including some llama.cpp distributions. Performance and feature coverage can differ from CUDA or ROCm, and a Vulkan build may expose different controls for layer offload and memory reporting.
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How to verify a 27B model before committing to a GPU
- Identify the exact model file. Record the architecture, parameter count, quantization (for example, Q4_K_M), and file size. Do not use a generic “27B” label as the memory estimate.
- Confirm backend support. Check whether your chosen application supports CUDA, ROCm, or Vulkan on the operating system and GPU you plan to use.
- Start with a conservative context. Load the model with a modest context setting, then watch the runtime’s VRAM allocation while generating several responses.
- Increase context gradually. Raise the context in measured steps until you reach the workload you need or encounter an out-of-memory error. Keep a margin for the desktop and temporary buffers.
- Check layer placement. A fully GPU-offloaded load avoids PCIe transfers during generation. If the runtime reports CPU-offloaded layers, note the speed and decide whether a smaller quantization or larger-memory card is preferable.
- Measure your own prompts. Token speed changes with prompt length, output length, batch size, sampling settings, and runtime version. Compare cards using the same model file and settings.
Memory planning by workload
| Workload | 24GB card such as RTX 4090 or RX 7900 XTX | 32GB RTX 5090 |
|---|---|---|
| 27B Q4 model, moderate context | Generally practical when the file is around 18–19GB and runtime overhead fits | Comfortable, with more room for context and buffers |
| 27B Q5 or Q6 model | May fit, but depends on exact file size and context; margin can be small | More likely to fit without CPU offload |
| Very long context | Constrained by KV-cache growth after weights load | Additional VRAM provides more flexibility, but does not remove all limits |
| Models above the single-card memory limit | Requires CPU offload, multiple GPUs, or another memory architecture | Same requirement once the model and context exceed 32GB |
These are planning categories, not guarantees. Quantization format, context implementation, batch size, and runtime overhead can move a workload from one category to another.
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Power, cooling, and total system cost
Compare the complete build rather than the graphics-card price alone. October 2026 reference prices cited for these guides are estimates, not guaranteed offers; check current listings, stock, warranty coverage, and regional taxes before purchase.
- GPU: include the card’s actual street price and warranty.
- Power supply: the 5090 guidance calls for at least 1,000W. A 4090 or 7900 XTX may work with a lower-rated unit depending on the rest of the system, but size it for sustained load and transient demand.
- Case and cooling: verify radiator, fan, and expansion-slot clearance. Inference can sustain high utilization for minutes or hours.
- Electricity: a high-power card used continuously costs more to operate than a lower-power system, even when the initial purchase is cheaper.
- Platform changes: an AMD purchase may require time for backend configuration; an NVIDIA purchase may cost more but reduce setup labor for CUDA-centric tools.
Multi-GPU and larger-memory options
If your target model or context exceeds 24–32GB, no single card in this comparison solves the capacity problem. Multi-GPU setups can split weights, but they add configuration complexity and communicate over PCIe. The slowest transfer path and synchronization pattern can reduce the benefit of a second card.
Unified-memory or workstation-class systems may provide a larger address space, but they have different cost, bandwidth, and software trade-offs. The cited single-card measurements do not establish a controlled multi-GPU result, so treat those systems as a separate evaluation rather than extrapolating from the 4090 or 5090 numbers.
Common failure modes and fixes
The model loads, then fails when generation starts
The KV cache or temporary buffers consumed the remaining VRAM. Lower the context, reduce batch size, close other GPU applications, or switch to a smaller quantization.
The runtime uses the CPU despite a compatible GPU
Install the backend-specific build, update the driver, and confirm device detection in the runtime’s startup log. On AMD, verify ROCm or Vulkan support rather than using a CUDA-only package.
Generation is much slower than the published number
Check whether all layers are on the GPU, then match the benchmark’s model file, quantization, context, batch size, and runtime. Published figures are workload-specific; the 65.66, 43.06, and 37.65 tokens-per-second values are not universal ratings.
The 5090 system shuts down or becomes unstable
Recheck the 575W-class board power requirement, the recommended 1,000W supply guidance, connector seating, and case airflow. Test with sustained inference rather than a brief desktop workload.
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Decision guide
- Buy the RTX 4090 when you want the least disruptive CUDA upgrade, your models fit in 24GB, and the measured 43.06-token-per-second class of performance is sufficient.
- Buy the RTX 5090 when you value the measured 65.66-token-per-second result, need 32GB for Q6 or longer contexts, and can accommodate its 575W power requirement and 1,000W-class supply guidance.
- Buy the RX 7900 XTX when 24GB is enough, your applications support ROCm or Vulkan, and you accept more backend-specific setup than a CUDA system.
- Keep or supplement the RTX 3090 when the model already fits and your bottleneck is not generation speed. Replacing it will not increase capacity unless you move to a card with more than 24GB.
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