24 GB vs 32 GB GPU for Local LLMs: The $1,500 Mistake Most Builders Make
The jump from 24 GB to 32 GB is the single biggest VRAM decision for local LLM users. It determines whether you run 70B models at Q4 entirely on GPU or rely on partial CPU offloading. Here is the math.
24 GB vs 32 GB: the specs
| Specification | 24 GB (Used RTX 4090) | 32 GB (RTX 5090) |
|---|---|---|
| VRAM | 24 GB GDDR6X | 32 GB GDDR7 |
| Bandwidth | 1,008 GB/s | 1,792 GB/s |
| Price | ~$1,200 (used) | $1,999 (new) |
| TDP | 450 W | 575 W |
| Warranty | None (used) | Full |
| PSU Needed | 850 W | 1,000 W |
What the 8 GB gap changes
For models under 24 GB (Mixtral 8x7B, Qwen 32B, Command R 35B), both tiers fit the model entirely. The 32 GB advantage there is purely speed from higher bandwidth: the RTX 5090 generates tokens ~70% faster than the RTX 4090 due to 1,792 vs 1,008 GB/s. The real decision is about 70B models: 24 GB requires significant offloading, 32 GB requires minor offloading. On Ollama and llama.cpp, both cards work with the same software stack — the difference is purely hardware.
Model by model comparison
| Model | 24 GB | 32 GB | Key Difference |
|---|---|---|---|
| Mixtral 8x7B Q4 | ~14 GB, ~50 t/s | ~14 GB, ~85 t/s | 1.7x speed |
| Qwen 2.5 32B Q4 | ~18 GB, ~35 t/s | ~18 GB, ~60 t/s | 1.7x speed |
| Command R 35B Q4 | ~20 GB, ~28 t/s | ~20 GB, ~50 t/s | 1.8x speed |
| Llama 70B Q3 | ~30 GB, partial offload | ~30 GB, fits entirely | No offload |
| Llama 70B Q4 | ~38 GB, heavy offload | ~38 GB, minor offload | 8 GB closer |
Cost per GB analysis
| GPU | Price | VRAM | Cost/GB |
|---|---|---|---|
| Used RTX 4090 | ~$1,200 | 24 GB | $50/GB |
| RTX 5090 | $1,999 | 32 GB | $62/GB |
The used RTX 4090 is cheaper per GB, but the RTX 5090 buys you 78% more bandwidth and the extra 8 GB that makes 70B Q4 viable with minimal offloading. If you run 70B models regularly, the $800 premium is justified by the speed and VRAM headroom alone.
Which should you choose?
- -Buy 24 GB (used RTX 4090) if you mostly run models under 35B, 70B at Q3 quality is acceptable, budget is under $1,500, and you are comfortable buying used.
- -Buy 32 GB (RTX 5090) if you regularly run 70B at Q4 or higher, need long context with large models, want the fastest token generation, and want a new card with warranty.
For specific GPU recommendations, see Best GPU for Local LLMs or the RTX 5090 vs RTX 4090 comparison.
Not sure which tier you need?
Use the VRAM Calculator to plug in your target model and context length — it will tell you whether 24 GB or 32 GB is the right call, and which specific GPUs are compatible.
Frequently Asked Questions
Is the RTX 5090 worth the premium over a used RTX 4090?
Can two 24 GB GPUs replace one 32 GB GPU?
Does GDDR7 make a real difference vs GDDR6X for LLMs?
End of Document
Reader Discussion
Be the first to add a note to this article.
Please log in to join the discussion.
No comments yet.
Related Articles
Best AMD vs Best NVIDIA GPU for Local LLMs: Where AMD Wins, and Where CUDA Still Controls the Market
Can Your GPU Run It? VRAM Compatibility Checker for 80+ LLMs
Used RTX 3090 vs New Midrange GPU for Local LLMs: Why the 3090 Wins on Value
