GPU comparison
RTX 3090 vs RTX 4090 for Local LLM
Draft comparison for future runtime-specific local LLM testing.
Planning summary only. Verify exact GPU variants, runtime support, and workload behavior before making purchase decisions.
RTX 3090
- VRAM
- 24 GB
- Memory type
- GDDR6X
- Bandwidth
- 936.2 GB/s
- Board power / TGP
- 350 W
RTX 4090
- VRAM
- 24 GB
- Memory type
- GDDR6X
- Bandwidth
- 1008 GB/s
- Board power / TGP
- 450 W
FAST ANSWER
Start with the decision that actually changes your workflow
Use this comparison when the capacity tier is already 24 GB and the user needs to think about bandwidth, power, architecture, and validation workflow instead of VRAM alone.
Is 24 GB enough for the target model?
Start with the calculator and model pages; this comparison only helps once the workload belongs in the 24 GB planning band.
Do not infer speed from generation alone
The page can compare source-backed planning fields, but speed claims need benchmark evidence for the exact workload.
Why this comparison matters
Both profiles sit at 24 GB, so the useful question shifts from capacity to bandwidth, power, generation, and runtime validation.
This pair belongs after a model estimate suggests that 12 GB or 16 GB cards are too constrained for the intended local LLM workflow.
The comparison helps frame why an older high-VRAM card can remain relevant while still requiring exact-card and power checks.
Quick planning summary
RTX 3090: 24 GB | RTX 4090: 24 GB
RTX 3090: 936.2 GB/s | RTX 4090: 1008 GB/s
RTX 3090: 350 W | RTX 4090: 450 W
Local LLM planning
RTX 3090: medium | RTX 4090: medium
Benchmark evidence, exact board-partner variant, runtime compatibility, and workload fit.
Comparison table
| Field | RTX 3090 | RTX 4090 |
|---|---|---|
| Memory planning | ||
| VRAM | 24 GB | 24 GB |
| Memory type | GDDR6X | GDDR6X |
| Memory bus | 384-bit | 384-bit |
| Memory bandwidth | 936.2 GB/s | 1008 GB/s |
| Compute / architecture | ||
| Vendor | NVIDIA | NVIDIA |
| Architecture | Ampere | Ada Lovelace |
| Core / execution units | 10496 CUDA cores | 16384 CUDA cores |
| Power planning | ||
| Board power / TGP | 350 W | 450 W |
| Power connector | Needs verification | 1 x PCIe Gen5 (or 3 x 8-pin adapter) |
| Verification | ||
| Status | Source-backed GPU specs available | Source-backed GPU specs available |
| Data confidence | medium | medium |
| Last verified | 2026-05-29 | 2026-05-29 |
Source-backed planning signals
RTX 4090 has higher listed memory bandwidth than RTX 3090. RTX 3090 has the lower listed power planning figure.
Use the RTX 3090 vs RTX 4090 signals as prompts for the validation sections below; this component does not add benchmark, price, availability, or purchase claims.
Use this page when these questions match your workflow
Start with the calculator and model pages; this comparison only helps once the workload belongs in the 24 GB planning band.
A high-VRAM shortlist should include PSU, case airflow, connector, and heat checks before treating either card as practical.
If the workload is important, validate with a cloud or borrowed setup before relying on a local purchase.
Before you trust the comparison
The page can compare source-backed planning fields, but speed claims need benchmark evidence for the exact workload.
An RTX 3090 path often raises condition, warranty, power, and thermal questions that the static comparison cannot settle.
Long context, larger models, batch size, and runtime overhead can exceed a simple VRAM-capacity assumption.
What the source-backed data shows
- Both linked GPU profiles expose 24 GB VRAM, making this a same-capacity planning comparison.
- The source-backed memory bandwidth values differ, but the page does not convert that into runtime speed claims.
- The comparison record has no benchmark source, so any final workload conclusion remains unresolved.
What still needs validation
- Which model, context length, quantization format, and runtime will be used?
- Can the local system handle the power, cooling, and physical card requirements?
- Would a short cloud validation run reduce risk before committing to local hardware?
PLANNING NEXT STEP
Check model memory before choosing between these GPUs
Run your model assumptions through the VRAM Calculator, then return to GPU profiles for source notes and board-partner verification.
Compare nearby GPU decisions next
Compare when the question is whether 16 GB is enough or 24 GB headroom is needed for image workflows.
Compare when a 24 GB decision also involves CUDA versus non-CUDA runtime support.
Compare if the calculator estimate may still fit below the 24 GB class.
FAQ
Why compare RTX 3090 and RTX 4090 if both have 24 GB VRAM?
Same-capacity comparisons are useful because the decision moves toward bandwidth, power planning, architecture, used-card risk, and runtime validation.
Can this page say which 24 GB GPU is faster for local LLMs?
No. The comparison does not include controlled benchmark evidence, so it avoids speed conclusions and keeps the focus on planning fields.
When should cloud testing come before buying either card?
Cloud testing is useful when the model, context length, quantization, or runtime setup is uncertain enough that a local purchase would be premature.
Related GPU profiles
Sources and data confidence
RTX 3090
Confidence: medium
Source types: official, database
RTX 4090
Confidence: medium
Source types: official, paper
No benchmark source is attached to this comparison, so benchmark claims are not included.