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Short answer: Both the GeForce RTX 3090 and RTX 4090 have 24GB of VRAM, so neither card has a capacity advantage for a one-GPU 27B model at the same quantization and context settings. A 4-bit 27B model can fit on either card when the model weights, KV cache, and runtime overhead remain within that 24GB budget. The RTX 4090 is faster in the directly comparable test available here: 43.06 tokens per second versus 37.65 tokens per second for the RTX 3090, with substantially lower first-token latency on a long prompt.
Choose the RTX 3090 when 24GB capacity at the lowest acceptable acquisition cost matters most. Choose the RTX 4090 when prompt-heavy use, interactive responsiveness, or higher throughput justifies paying more. Your practical limit is set less by the model’s advertised parameter count than by quantization, context length, KV-cache precision, backend, drivers, and runtime version.
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What the comparison actually tells you
A 27B local model is not automatically “too large” for a 24GB graphics card. The question is whether the complete inference workload fits: quantized model weights, the key-value (KV) cache for your chosen context, CUDA and backend allocations, and the application’s other memory needs. A file that loads at a short context can fail or become impractically slow after you increase the context window.
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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →At equal settings, the two cards share the same broad one-GPU capacity ceiling because each has 24GB of VRAM. The RTX 4090’s advantage is processing speed, not a larger model that it can hold. If your 27B quantization already fits comfortably on a 3090, moving to a 4090 normally improves response time rather than allowing a fundamentally larger quantization.
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Hardware specifications that affect local inference
| Specification | GeForce RTX 3090 | GeForce RTX 4090 | Why it matters for a 27B model |
|---|---|---|---|
| VRAM | 24GB GDDR6X | 24GB GDDR6X | Equal nominal capacity for model weights, KV cache and runtime allocations. |
| CUDA cores | 10,496 | Not stated in the cited Ada specification | Core count alone does not predict LLM performance; memory movement and software also matter. |
| Memory interface | 384-bit | Not stated in the cited specification | Interface width is one part of total memory behavior. |
| Memory bandwidth | Not stated in the cited specification | 1,008GB/s | Higher bandwidth can help move weights and cache data during inference. |
| Graphics-card power | 350W | Not stated in the cited specification | Power supply capacity, cooling and noise planning are essential, especially for sustained loads. |
NVIDIA’s official RTX 3090 product specification lists 24GB of GDDR6X, a 384-bit memory interface, 10,496 CUDA cores and 350W graphics-card power. NVIDIA’s Ada architecture specification lists 24GB of GDDR6X and 1,008GB/s of memory bandwidth for the RTX 4090. Those figures explain the unusual shape of this matchup: the cards have comparable capacity, while the newer card can process the workload faster.
Will a 27B model fit in 24GB?
What 4-bit means for the weight budget
At four bits per parameter, 27 billion parameters require about 13.5GB for raw weight data before file metadata, tensor alignment, temporary buffers and the inference runtime are included. A real GGUF or similar quantized file is therefore larger than the raw arithmetic suggests, but usually leaves meaningful room in a 24GB card for the runtime and a moderate context. That is why 4-bit 27B models are the practical starting point for a single 3090 or 4090.
“Fits” is not a binary property. A model may load at 4,096 tokens and fail at 32,768 tokens because the KV cache grows with the active context. A longer conversation, larger system prompt or batch size can consume the headroom that made the initial load successful. Keep a margin rather than targeting every last megabyte.
Quantization and cache choices
- 4-bit weights: Usually the realistic quality-and-capacity balance for a 27B model on one 24GB card.
- Higher-bit weights: Improve numerical fidelity in some workloads but consume more VRAM; an 8-bit representation of 27B parameters is roughly 27GB before overhead, so it cannot reside entirely on a 24GB card.
- KV-cache precision: Lower-precision caches can preserve more context headroom; higher precision uses more memory. The exact savings and quality trade-off depend on the backend and model.
- Context length: Increasing it allocates more KV cache. Set the maximum you actually need instead of accepting an unnecessarily large default.
When a model does not fit, partial CPU offload can make it launch, but moving layers or cache data across the PCIe bus generally reduces speed and changes the comparison. Treat a mixed CPU/GPU run as a different configuration, not as evidence that the full model fits on the GPU.
Measured speed: RTX 4090 versus RTX 3090
The directly comparable measurement used the same llama.cpp runtime and the same Qwen3.8-27B Q4_K_M model on both cards.
| Metric | RTX 3090 | RTX 4090 | Difference |
|---|---|---|---|
| Decode throughput | 37.65 tokens/s | 43.06 tokens/s | About 14% higher on the RTX 4090. |
| First-token latency on a long prompt | 2,921ms | 1,684ms | About 42% lower latency on the RTX 4090. |
The throughput result means the 4090 generated roughly five more tokens per second in that run. The latency result is more important for chat: before generation begins, the 4090 spent considerably less time processing the long prompt. A model can have acceptable steady-state speed yet still feel sluggish when every turn includes a large system prompt or conversation history.
These are measurements for one model file, quantization, runtime and test procedure in 2026, not universal specifications. They do not establish performance for every 27B architecture, context length or backend. Kernel selection, driver versions, CUDA libraries, GPU clocks, thermal limits and whether prompt processing is included can all change the result.
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Decode speed and prompt speed are different
Decode throughput
Decode throughput is the rate shown while the model generates one token after another. It determines how quickly a long answer appears once generation has started. A 14% advantage is noticeable but not transformational: a response that takes 20 seconds to generate on the 3090 would take about 17.5 seconds at the measured 4090 rate, assuming identical conditions.
Prompt processing and time to first token
Prompt processing reads the input context and builds the internal state needed for generation. Its cost rises with prompt length. The long-prompt measurement shows why a card can feel much faster even when decode tokens per second are only moderately higher. If your workflow repeatedly pastes documents, maintains long chat histories or runs retrieval-augmented prompts, test first-token latency separately from decode speed.
Concurrent requests
Neither cited test is a capacity benchmark for multiple simultaneous users. The 4090’s faster single-request processing may help a server clear work sooner, but concurrency also depends on batching, available VRAM for several KV caches, scheduler behavior and the model server. Do not infer a specific number of concurrent sessions from the two single-run figures.
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Running a 27B model locally with Ollama
Basic launch
Ollama’s Gemma 2 announcement confirms a 27B model variant and uses this command:
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsollama run gemma2:27bThe command requests the model; the actual placement and usable context depend on your installed Ollama version, model variant and available memory. Watch the startup output to confirm whether the model is fully resident on the GPU or has been split with system memory.
Why scheduler behavior matters
In a September 23, 2025 scheduling update, Ollama said the engine “will measure the exact amount of memory required” before running a model. That preflight behavior is useful because a model that technically fits at idle may not fit after the application, display driver and cache allocations are counted. Software changes can also alter how much is loaded on the GPU and the resulting token generation rate.
Record the configuration
For a meaningful 3090-versus-4090 comparison, record all of the following:
- Exact model name and model-file revision.
- Quantization format, such as Q4_K_M.
- Context length and batch size.
- KV-cache data type.
- Whether all layers are on the GPU or some are offloaded to the CPU.
- Backend and runtime version, including llama.cpp or Ollama.
- NVIDIA driver and CUDA component versions.
- Prompt-processing and decode results separately.
A repeatable test procedure
- Install the same runtime build on both systems. Do not compare a newly optimized build on one card with an older build on the other.
- Use the identical model file. A different quantizer, revision or tokenizer invalidates a direct speed comparison.
- Set the same context and cache precision. Otherwise one card may be doing less work or using a different amount of memory.
- Warm up the model. The first request may include file mapping, kernel initialization or cache setup that later requests do not.
- Measure prompt and generation phases separately. Capture time to first token, prompt-processing rate when available, and decode tokens per second.
- Repeat each measurement. Use several runs after warm-up and report the median or another clearly defined statistic rather than a favorable single result.
- Check GPU residency and temperature. A run that silently offloads layers or throttles clocks is not comparable with a full-speed run.
- Keep the prompt fixed. Use the same long prompt for latency tests and the same generation length for decode tests.
This procedure makes the published 43.06 versus 37.65 tokens-per-second result useful as a reference instead of treating it as a promise for every installation.
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Power, cooling and system requirements
RTX 3090 platform
The RTX 3090 is specified at 350W graphics-card power. A system built around it needs a power supply with enough continuous capacity for the GPU, CPU, drives and transient demand, plus appropriate PCIe power connectors. Sustained LLM inference keeps the card busy for long periods, so case airflow and heatsink condition matter more than a short gaming benchmark.
RTX 4090 platform
The cited Ada specification does not provide a board-power figure for the RTX 4090 in this comparison. Check the exact board partner model before choosing a power supply or case. Connector requirements, card thickness, radiator or fan clearance and cable bend radius vary by model. Do not assume that replacing a 3090 with a 4090 is a drop-in physical upgrade.
Noise and thermal throttling
Two cards can produce different fan noise at the same token rate because cooler design, ambient temperature and fan curves differ. Log clock speeds and temperatures during a sustained run. If tokens per second decline over time, improve intake and exhaust airflow, clean dust filters and verify that the cooler is making proper contact before drawing conclusions about the GPU architecture.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Value: decide from your workload, not the model name
No live street price is established here, so a universal “best deal” claim would be misleading. Compare the actual purchase price, seller, warranty, condition and expected service life of the specific cards available to you. Used RTX 3090 listings can vary widely in memory condition and warranty; 4090 pricing can vary by cooler and board design.
A simple value calculation is:
effective cost = purchase price + expected power, cooling and replacement costsThen ask what the measured time saving is worth. If you generate occasionally and the 3090’s 37.65 tokens/s feels responsive, spending more for 43.06 tokens/s may not repay itself. If you process long prompts all day, the reduction from 2,921ms to 1,684ms before generation may save more waiting time than the decode percentage suggests.
Which card fits each use case?
Choose the RTX 3090 when
- You need 24GB of VRAM at the lowest acceptable acquisition cost.
- Your chosen 4-bit 27B model and context fit with comfortable memory headroom.
- Most requests are short enough that the measured prompt-latency difference is unimportant.
- You already own a suitable power supply, case and cooling system.
Choose the RTX 4090 when
- Long prompts and interactive first-token latency dominate your experience.
- You serve requests frequently or want more decode throughput from one GPU.
- The card’s purchase premium is justified by the hours saved and the platform you already have.
- You can accommodate its exact physical dimensions, connectors and cooling requirements.
Consider two GPUs or a different model when
- You require a higher-bit 27B model that cannot fit in 24GB.
- You need very long contexts whose KV cache consumes the remaining headroom.
- You want several concurrent users and one card cannot hold all required KV caches.
- CPU offload produces response times that fail your application’s target.
Adding a second card changes memory topology, software configuration and power requirements. It is not equivalent to having one 48GB pool, and performance depends on how the runtime splits layers and communicates between GPUs.
Troubleshooting common failures
The model fails to load
- Reduce the context length and retry.
- Use a lower-precision KV cache if your backend supports it.
- Close other GPU applications and verify available VRAM before launch.
- Confirm that the runtime is not reserving an unexpectedly large batch or temporary buffer.
- Try a smaller quantization rather than forcing aggressive CPU offload.
The model loads but is much slower than expected
- Check whether some layers moved to system RAM.
- Confirm that the intended CUDA GPU backend is active.
- Compare the exact model file and quantization with the reference run.
- Check driver and runtime versions, GPU clocks and thermal throttling.
- Separate prompt-processing time from decode time; a long prompt can make the first response appear slow even when generation is normal.
Performance changes after an update
Record the runtime, driver and model versions before updating. Ollama’s memory-scheduling changes illustrate why software can alter GPU loading and token generation without any hardware change. Re-run the same fixed prompt and context after an update instead of comparing casual sessions.
Bottom line for a one-GPU 27B build
Both cards can be sensible hosts for a 4-bit 27B model because both provide 24GB of VRAM. The RTX 3090 delivers the lower-cost path to that capacity, while the RTX 4090 delivers higher measured decode throughput and markedly lower long-prompt first-token latency. Capacity, however, remains tied: context length, KV-cache precision and runtime overhead decide whether the model remains practical after it loads.
Buy based on your measured response-time requirement and the complete platform cost. If the 3090 meets your target, its equal VRAM makes it the rational value choice. If waiting on long prompts or serving frequent requests is the bottleneck, the 4090’s speed advantage can justify its premium without pretending that it enables a larger one-GPU model.
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