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Both cards can host a 27B model on one GPU at the same quantization because each has 24GB of VRAM. The RTX 4090 is faster in the published like-for-like test—43.06 versus 37.65 generated tokens per second—and responds sooner to long prompts, while the RTX 3090 can be the better value when acquisition cost matters more than latency.
The short answer
A 24GB RTX 3090 or 24GB RTX 4090 gives a 27B model the same broad one-GPU capacity ceiling. VRAM capacity alone does not make the 4090 capable of loading a larger quantization: both cards have 24GB of GDDR6X. The practical limit is the combined size of model weights, the key-value (KV) cache for your context window, and runtime overhead.
In a same-model llama.cpp comparison using Qwen3.8-27B in Q4_K_M quantization, the RTX 4090 generated 43.06 tokens per second and the RTX 3090 generated 37.65 tokens per second. That is roughly a 14% decode-throughput advantage for the 4090 in that test. On a long prompt, first-token latency was 1,684 milliseconds on the 4090 versus 2,921 milliseconds on the 3090, a larger improvement for interactive use.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Choose the 3090 when you can buy a reliable 24GB card for substantially less and its response time meets your needs. Choose the 4090 when prompt-heavy work, lower time-to-first-token, or more simultaneous generation justifies paying more for the card and the rest of the system. Neither card should be selected on the expectation of extra model capacity; their VRAM capacity is equal.
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What a 27B local model actually needs
Weights are only the first allocation
“27B” describes approximately 27 billion parameters, not the amount of GPU memory the running application will consume. Quantization stores those parameters with fewer bits. A four-bit representation has a theoretical weight payload of about 13.5GB before scale data, metadata, tensor alignment, and the particular quantizer’s overhead. The downloaded file is therefore larger than the simple parameter-count calculation, and the runtime allocates additional buffers.
The total VRAM requirement is better represented as:
model weights + KV cache + execution and workspace buffers + runtime overhead.
The KV cache grows as the context window grows. A short exchange may fit comfortably after the model is loaded, while a long document, codebase, or conversation can consume several additional gigabytes. KV-cache precision also matters: a lower-precision cache reduces memory use, while a higher-precision cache can preserve more numerical accuracy at the cost of capacity.
Why a nominal fit can still fail
- Context length: Increasing the maximum context reserves or grows the KV cache. The same Q4_K_M model that loads at a modest context can run out of memory at a much longer one.
- Quantization variant: “4-bit” is a family of formats, not one fixed size. Q4_K_M, other K-quants, and different file implementations have different overheads and quality trade-offs.
- Backend and runtime: GPU kernels, temporary buffers, and offload behavior vary between
llama.cpp, Ollama, driver versions, and builds. - Concurrent sessions: Multiple active requests need separate cache space or additional scheduling resources. A single-user fit does not prove that several users will fit simultaneously.
- Operating-system allocation: Desktop display use and other GPU applications reduce the memory available to inference.
For a one-GPU 27B setup, treat 24GB as a budget to manage rather than a guarantee. Leave headroom instead of configuring the entire nominal capacity for weights.
RTX 3090 and RTX 4090 hardware relevant to inference
| Specification | GeForce RTX 3090 | GeForce RTX 4090 |
|---|---|---|
| VRAM | 24GB GDDR6X | 24GB GDDR6X |
| Memory bandwidth | Not listed in the cited RTX 3090 specification used for this comparison | 1,008GB/s |
| CUDA cores | 10,496 | Not stated in the cited Ada specification |
| Graphics-card power | 350W | Not stated in the cited Ada specification |
| Architecture reference | NVIDIA RTX 3090 product specification | NVIDIA Ada GPU Architecture specification |
NVIDIA’s RTX 3090 specification identifies a 384-bit memory interface, 10,496 CUDA cores, 24GB of GDDR6X, and 350W graphics-card power. NVIDIA’s Ada specification identifies the RTX 4090’s 24GB of GDDR6X and 1,008GB/s memory bandwidth. Those are specification-level differences, not a promise that every model or backend will scale by the same percentage.
Measured speed: RTX 4090 versus RTX 3090
The most useful numbers here come from one controlled comparison rather than from unrelated reviews. The run used the same Qwen3.8-27B Q4_K_M model and the llama.cpp runtime on both GPUs.
| Measurement | RTX 4090 | RTX 3090 | Difference |
|---|---|---|---|
| Decode throughput | 43.06 tokens/s | 37.65 tokens/s | About 14% higher on the RTX 4090 |
| First-token latency on a long prompt | 1,684ms | 2,921ms | 1,237ms lower on the RTX 4090 |
How to interpret 43.06 versus 37.65 tokens per second
Decode throughput describes the rate after prompt processing, while the first-token figure includes the wait before the model begins producing visible output for the tested long prompt. At 37.65 tokens per second, a 100-token answer would take about 2.7 seconds of generation after decoding starts; at 43.06 tokens per second, it would take about 2.3 seconds. The difference is noticeable, but it does not turn the 3090 into an unusable card.
The larger latency difference matters when you repeatedly submit long prompts: retrieval-augmented context, large source files, long chat histories, and agent tool traces. A user may perceive the 4090 as much more responsive even when the steady stream of generated text is only about 14% faster.
Why this is not a universal speed guarantee
The figures apply to the named model, Q4_K_M quantization, runtime, and test conditions. Changing any of the following can alter both GPUs’ results:
- the exact model file or quantization;
- context length and prompt size;
- KV-cache precision;
- GPU offload layers and backend;
- NVIDIA driver and runtime versions;
- clock, temperature, power-limit, and cooling behavior;
- whether the reported number is decode-only or includes prompt processing.
Use the result as a directional comparison, not as a replacement for measuring your own workload.
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Usually, a 27B four-bit model is the sensible starting point for a 24GB single-GPU configuration, but “fits” has two meanings:
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- Load fit: the model weights and startup allocations can be placed in VRAM.
- Working fit: the chosen context, KV cache, temporary buffers, and concurrent requests remain in VRAM during actual use.
Both cards pass the same capacity test because both have 24GB. The 4090 does not provide extra room for a larger context or a higher-precision 27B quantization. If a configuration fails on the 3090 because of memory capacity, it will generally fail on the 4090 for the same reason unless a software change alters allocation behavior.
A practical fit procedure
- Start with the exact 27B quantization you intend to use, such as Q4_K_M, rather than estimating from the parameter count alone.
- Set a conservative context length and confirm that the model loads with GPU offload enabled.
- Run a prompt close to your real workload, not only an empty chat. Watch for allocation failure as the KV cache grows.
- Increase context in measured steps until quality or capacity becomes unacceptable.
- If you need more headroom, lower the context, use a more memory-efficient KV-cache type, reduce concurrent sessions, or select a smaller quantization.
Do not infer capacity from the model’s file size alone. A file can be smaller than free VRAM while the runtime still needs additional workspace and cache memory.
Running a 27B model with Ollama
The basic launch command
Ollama documents a 27B Gemma 2 variant that can be started with:
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ollama run gemma2:27bThe command identifies the model tag, but it does not by itself guarantee that the entire workload will reside on the GPU. Placement depends on the model build, available VRAM, context settings, and Ollama’s scheduler.
What the scheduler changes
Ollama says its newer scheduler measures the exact memory requirement before running a model. That behavior is important for 24GB cards: the engine may decide that a configuration requires partial CPU placement or that another loaded model must be removed. Software changes can therefore affect GPU loading and token generation even when the graphics card is unchanged.
A repeatable Ollama check
- Close other GPU-heavy applications and start only the target model.
- Record the model tag, quantization or file variant, context setting, Ollama version, NVIDIA driver, and whether another model is resident.
- Send a short prompt to test basic loading, then a prompt representative of your normal long-context use.
- Observe whether generation remains stable as the conversation grows. An initial successful load is not proof that the largest context will fit.
- Measure several runs rather than relying on one displayed speed, because startup, prompt processing, and decode phases are different measurements.
Using llama.cpp for a controlled comparison
llama.cpp is useful when you want to hold the model and quantization constant while changing only the GPU. For a valid comparison, keep the following fixed:
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- the exact Qwen3.8-27B Q4_K_M file;
- context length and batch-related settings;
- KV-cache precision;
- GPU-layer offload configuration;
- backend and build version;
- NVIDIA driver and operating-system environment;
- prompt text and output length.
Report prompt processing and decode separately. A benchmark that reports only tokens per second can hide a large first-token delay, while a latency-only result says little about the speed of a long generated answer.
Benchmarking checklist
- Warm up the application so one-time compilation or initialization does not dominate the result.
- Use the same prompt and generation settings on both cards.
- Run multiple trials and compare the median or a clearly stated aggregate.
- Record GPU temperature, clock behavior, and power settings when thermal throttling is possible.
- Publish the model file, quantization, context length, KV-cache type, backend, driver, runtime version, and whether prompt processing was included.
Without those details, a number such as “40 tokens per second” is difficult to reproduce or compare.
Power, cooling, and system requirements
NVIDIA lists the RTX 3090 at 350W graphics-card power. That figure affects more than the electricity bill: the power supply must have appropriate capacity and connectors, the case must provide sustained airflow, and fan noise can become part of the user experience during long inference sessions.
The cited Ada specification establishes the RTX 4090’s memory and bandwidth but does not provide a comparable graphics-card power figure in the supplied specifications. Check the exact board partner model for its power requirement, connector arrangement, dimensions, and cooling design before buying.
For either card, verify:
- physical clearance for the card and its power cable;
- a power supply sized for the complete system, not only the GPU;
- front-to-back case airflow and unobstructed intake;
- stable temperatures during a sustained generation test;
- driver support for the backend you plan to use.
A card that reaches its thermal or power limit can deliver lower throughput than the same model in a better-cooled system. This is one reason to record operating conditions with your benchmark.
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Value: when the faster card is worth it
No live street price is established for this comparison, so a value decision must use the actual listings available to you. Compare the complete acquisition: purchase price, warranty, seller reputation, card condition, expected remaining life, and any required power-supply or case upgrades.
The RTX 3090 is the value choice when
- you need 24GB of VRAM at the lowest acceptable acquisition cost;
- the measured 37.65-token-per-second class of performance is adequate;
- your prompts are not so long that first-token latency dominates the workflow;
- you already own a suitable power supply and cooling setup;
- you value capacity per dollar more than peak responsiveness.
The RTX 4090 is worth paying more for when
- long prompts are routine and the lower measured first-token latency saves meaningful waiting;
- you generate continuously enough for the throughput difference to compound over many hours;
- you need more responsive interactive coding, document, or agent sessions;
- you run multiple workloads and the faster card improves scheduling time, even though VRAM capacity is unchanged;
- the price premium is justified by warranty, condition, and expected service life rather than by extra model capacity.
A simple break-even calculation
Estimate the hours you will spend waiting for inference over the card’s useful life. The 4090’s measured decode advantage is about 5.41 tokens per second in this test, while its long-prompt first-token advantage is 1,237 milliseconds. If those saved seconds have little value to you, paying a premium is difficult to justify. If you run long prompts all day, convert the saved time into an hourly or monthly value and compare it with the complete price difference.
Common failure modes and fixes
Out-of-memory during startup
Reduce context length, close other GPU applications, lower the KV-cache precision if your runtime supports it, or select a smaller quantization. Confirm that the runtime is not reserving an unexpectedly large workspace. Moving some layers to system memory can allow startup, but it changes latency and throughput and should be measured rather than assumed to be equivalent to full GPU offload.
The model loads but fails on long conversations
The growing KV cache is the likely cause. Lower the maximum context, periodically start a fresh conversation, summarize older turns, or reduce concurrent sessions. Test with the longest prompt you expect rather than the short startup prompt.
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Check that the intended GPU backend is active and that the model is actually offloaded as expected. Then compare the exact quantization, context, driver, runtime version, and prompt type. A decode-only number cannot be compared directly with a result that includes prompt processing. Thermal throttling, conservative power limits, and CPU offload can also reduce speed.
First-token latency is unexpectedly high
Separate prompt processing from decode. Long prompts require more prefill work, and a large context can make the first response slow even when generated tokens stream quickly afterward. Measure a short and a long prompt independently and report both numbers.
Performance changes after a software update
Record runtime and driver versions with every benchmark. Scheduler and memory-accounting changes can alter GPU loading, workspace allocation, and token generation without any hardware change. Re-run the same fixed workload after updates instead of comparing an old headline number with a new one.
Decision matrix
| Your priority | Better starting point | Reason |
|---|---|---|
| Lowest cost for 24GB capacity | RTX 3090 | It provides the same 24GB capacity ceiling; choose based on the actual card’s price and condition. |
| Lower first-token latency | RTX 4090 | 1,684ms versus 2,921ms in the cited long-prompt test. |
| Higher decode throughput | RTX 4090 | 43.06 versus 37.65 tokens/s on Qwen3.8-27B Q4_K_M. |
| Largest model that fits on one GPU | Neither has an inherent advantage | Both cards have 24GB; context, KV-cache settings, and runtime overhead decide the practical fit. |
| Existing 350W-class cooling and power setup | RTX 3090 may be simpler | NVIDIA lists the 3090 at 350W; verify the exact 4090 board’s requirements before installation. |
Bottom line
For local 27B inference, the RTX 3090 and RTX 4090 start from the same 24GB VRAM ceiling. A four-bit 27B model can be a practical one-GPU target on either card, but context length, KV-cache precision, and runtime overhead determine whether it remains usable. The RTX 4090 is demonstrably faster in the cited controlled run—about 14% higher decode throughput and substantially lower long-prompt first-token latency. Buy it for measurable responsiveness and sustained throughput; buy the RTX 3090 when a lower total acquisition cost matters more and its speed meets your workload.
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