RTX 5080 16GB vs RTX 4090 24GB for local LLMs
Memory fit, speed and context compared with the same math as the advisor. RTX 4090 24GB has 8.0 GB more usable memory (15.0 vs 23.0 GB) and higher bandwidth than the other (1008 vs 960 GB/s).
Verdict from the numbers
- Fit. RTX 5080 16GB fits 5 of 13 models fully at 16k context (largest: gpt-oss 20B Q8_0); RTX 4090 24GB fits 11 (largest: Qwen3.6 35B-A3B UD-Q4_K_M). Only RTX 4090 24GB fits fully: Gemma 4 26B-A4B, Gemma 4 31B, Qwen3.6 27B, Qwen3.6 35B-A3B, Qwen3-Coder 30B-A3B, Mistral Small 3.2 24B.
- Speed. On the 5 models that fit fully on both, RTX 4090 24GB is faster by a typical 1.4x. RTX 5080 16GB is ahead on 1, RTX 4090 24GB on 4. No model is measured on both machines, so every comparison involves an estimate; read it as a guide.
- Trade-off. RTX 4090 24GB has 8.0 GB more usable memory (15.0 vs 23.0 GB) and higher bandwidth than the other (1008 vs 960 GB/s).
Where to check prices
Price tier note
We do not list prices because they move daily and differ between new and used stock. What the data does show is what the memory difference buys: RTX 4090 24GB has 8.0 GB more usable memory, which moves 6 of 13 models into full-GPU fit (Gemma 4 26B-A4B, Gemma 4 31B, Qwen3.6 27B, Qwen3.6 35B-A3B, Qwen3-Coder 30B-A3B, Mistral Small 3.2 24B). Use the Amazon links above to compare current listings and decide whether the difference is worth it for the models you want to run.
Model by model · 16k context, f16 KV cache
| Model | RTX 5080 16GB fit | tok/s | RTX 4090 24GB fit | tok/s | Faster / fits |
|---|---|---|---|---|---|
| Gemma 4 26B-A4B UD-Q4_K_M | MoE offload to CPU | 102 estimated | fits fully | 135 scaled from measured | RTX 4090 24GB (fits) |
| Gemma 4 31B Q4_K_M | partial offload (slow) | 9 estimated | fits fully | 44 measured | RTX 4090 24GB (fits) |
| Gemma 4 12B Q8_0 | fits fully | 39 scaled from measured | fits fully | 64 measured | RTX 4090 24GB |
| Qwen3.6 27B Q4_K_M | partial offload (slow) | 14 estimated | fits fully | 42 scaled from measured | RTX 4090 24GB (fits) |
| Qwen3.6 35B-A3B UD-Q4_K_M | MoE offload to CPU | 72 estimated | fits fully | 190 measured | RTX 4090 24GB (fits) |
| Qwen3.5 9B Q8_0 | fits fully | 90 scaled from measured | fits fully | 95 measured | RTX 4090 24GB |
| Qwen3.5 4B Q8_0 | fits fully | 183 scaled from measured | fits fully | 158 measured | RTX 5080 16GB |
| gpt-oss 20B Q8_0 | fits fully | 187 scaled from measured | fits fully | 258 measured | RTX 4090 24GB |
| gpt-oss 120B | does not fit | - | does not fit | - | - |
| Qwen3-Coder 30B-A3B Q4_K_M | MoE offload to CPU | 81 estimated | fits fully | 266 measured | RTX 4090 24GB (fits) |
| Mistral Small 3.2 24B Q4_K_M | partial offload (slow) | 20 estimated | fits fully | 42 scaled from measured | RTX 4090 24GB (fits) |
| Qwen3-VL 8B Q8_0 | fits fully | 34 scaled from measured | fits fully | 102 scaled from measured | RTX 4090 24GB |
| GLM-4.5-Air | does not fit | - | does not fit | - | - |
Badges: measured medians of real llama.cpp runs; scaled from measured and estimated come from our bandwidth model. See sources and methods.
How much context fits?
Largest context where weights plus KV cache stay inside usable memory, per model that fits fully on at least one side. q8_0 KV roughly halves the cache.
| Model | RTX 5080 16GB f16 | RTX 4090 24GB f16 | RTX 5080 16GB q8_0 | RTX 4090 24GB q8_0 |
|---|---|---|---|---|
| Qwen3.6 35B-A3B | - | 32k | - | 64k |
| Gemma 4 31B | - | 32k | - | 64k |
| Qwen3-Coder 30B-A3B | - | 32k | - | 64k |
| Qwen3.6 27B | - | 32k | - | 64k |
| Gemma 4 26B-A4B | - | 64k | - | 128k |
| Mistral Small 3.2 24B | - | 16k | - | 32k |
| gpt-oss 20B | 64k | 128k | 128k | 128k |
| Gemma 4 12B | 64k | 256k | 128k | 256k |
| Qwen3.5 9B | 128k | 256k | 256k | 512k |
| Qwen3-VL 8B | 32k | 64k | 64k | 128k |
| Qwen3.5 4B | 256k | 512k | 512k | 1M |
Spec summary
| RTX 5080 16GB | RTX 4090 24GB | |
|---|---|---|
| Usable memory for model + KV cache | 15.0 GB | 23.0 GB |
| Memory bandwidth | 960 GB/s | 1008 GB/s |
| Models that fit fully at 16k | 5 of 13 | 11 of 13 |
| Models that need offload or do not fit | 8 | 2 |
| Measured data points on file | 11 | 24 |
FAQ
Which is faster for local LLMs, RTX 5080 16GB or RTX 4090 24GB?
On the 5 models that fit fully on both, RTX 4090 24GB is typically 1.4x faster (geometric mean). RTX 5080 16GB wins 1 and RTX 4090 24GB wins 4. 0 comparisons are measured on both.
Which fits more models, RTX 5080 16GB or RTX 4090 24GB?
RTX 5080 16GB fits 5 of 13 models fully at 16k context and RTX 4090 24GB fits 11. Only RTX 4090 24GB: Gemma 4 26B-A4B, Gemma 4 31B, Qwen3.6 27B, Qwen3.6 35B-A3B, Qwen3-Coder 30B-A3B, Mistral Small 3.2 24B.
Can I run long context on RTX 5080 16GB or RTX 4090 24GB?
At f16 KV cache, the largest-model context limits are in the table above (for example gpt-oss 20B on RTX 5080 16GB: 64k; Qwen3.6 35B-A3B on RTX 4090 24GB: 32k). A q8_0 KV cache extends it further.
Do you state prices for RTX 5080 16GB or RTX 4090 24GB?
No. Prices change daily, so we link to Amazon search results instead and focus on measured and estimated speed and memory fit.
Related
RTX 5080 16GB · RTX 4090 24GB · Measured model pages: gpt-oss 20B · Qwen3.6 35B-A3B · Qwen3-Coder 30B-A3B · Gemma 4 31B · Qwen3.6 27B · Gemma 4 26B-A4B
More comparisons: RTX 3090 24GB vs RTX 4090 24GB · RTX 4090 24GB vs RTX 5090 32GB · RTX 4090 24GB vs AMD RX 7900 XTX 24GB · MacBook Pro 14/16 (2024) M4 Max 128GB vs RTX 4090 24GB · all comparisons
By memory: 8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB