2x RTX 3090 (48GB total) vs RTX PRO 6000 96GB (bonus page) for local LLMs
Memory fit, speed and context compared with the same math as the advisor. RTX PRO 6000 96GB (bonus page) has 49.0 GB more usable memory (46.0 vs 95.0 GB) and higher bandwidth than the other (1792 vs 936 GB/s).
Verdict from the numbers
- Fit. 2x RTX 3090 (48GB total) fits 11 of 13 models fully at 16k context (largest: Qwen3.6 35B-A3B Q8_0); RTX PRO 6000 96GB (bonus page) fits 13 (largest: gpt-oss 120B Q8_0). Only RTX PRO 6000 96GB (bonus page) fits fully: gpt-oss 120B, GLM-4.5-Air.
- Speed. On the 11 models that fit fully on both, RTX PRO 6000 96GB (bonus page) is faster by a typical 1.3x. 2x RTX 3090 (48GB total) is ahead on 4, RTX PRO 6000 96GB (bonus page) on 7. No model is measured on both machines, so every comparison involves an estimate; read it as a guide.
- Trade-off. RTX PRO 6000 96GB (bonus page) has 49.0 GB more usable memory (46.0 vs 95.0 GB) and higher bandwidth than the other (1792 vs 936 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 PRO 6000 96GB (bonus page) has 49.0 GB more usable memory, which moves 2 of 13 models into full-GPU fit (gpt-oss 120B, GLM-4.5-Air). 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 | 2x RTX 3090 (48GB total) fit | tok/s | RTX PRO 6000 96GB (bonus page) fit | tok/s | Faster / fits |
|---|---|---|---|---|---|
| Gemma 4 26B-A4B Q8_0 | fits fully | 125 estimated | fits fully | 215 estimated | RTX PRO 6000 96GB (bonus page) |
| Gemma 4 31B Q8_0 | fits fully | 19 estimated | fits fully | 33 estimated | RTX PRO 6000 96GB (bonus page) |
| Gemma 4 12B Q8_0 | fits fully | 49 estimated | fits fully | 44 scaled from measured | 2x RTX 3090 (48GB total) |
| Qwen3.6 27B Q8_0 | fits fully | 22 estimated | fits fully | 39 estimated | RTX PRO 6000 96GB (bonus page) |
| Qwen3.6 35B-A3B Q8_0 | fits fully | 163 estimated | fits fully | 281 estimated | RTX PRO 6000 96GB (bonus page) |
| Qwen3.5 9B Q8_0 | fits fully | 67 estimated | fits fully | 60 scaled from measured | 2x RTX 3090 (48GB total) |
| Qwen3.5 4B Q8_0 | fits fully | 143 estimated | fits fully | 93 scaled from measured | 2x RTX 3090 (48GB total) |
| gpt-oss 20B Q8_0 | fits fully | 241 estimated | fits fully | 287 scaled from measured | RTX PRO 6000 96GB (bonus page) |
| gpt-oss 120B Q4_K_M | MoE offload to CPU | 55 estimated | fits fully | 196 scaled from measured | RTX PRO 6000 96GB (bonus page) (fits) |
| Qwen3-Coder 30B-A3B Q8_0 | fits fully | 143 estimated | fits fully | 246 estimated | RTX PRO 6000 96GB (bonus page) |
| Mistral Small 3.2 24B Q8_0 | fits fully | 26 estimated | fits fully | 44 estimated | RTX PRO 6000 96GB (bonus page) |
| Qwen3-VL 8B Q8_0 | fits fully | 74 estimated | fits fully | 66 scaled from measured | 2x RTX 3090 (48GB total) |
| GLM-4.5-Air Q4_K_M | MoE offload to CPU | 14 estimated | fits fully | 95 estimated | RTX PRO 6000 96GB (bonus page) (fits) |
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 | 2x RTX 3090 (48GB total) f16 | RTX PRO 6000 96GB (bonus page) f16 | 2x RTX 3090 (48GB total) q8_0 | RTX PRO 6000 96GB (bonus page) q8_0 |
|---|---|---|---|---|
| gpt-oss 120B | - | 128k | - | 128k |
| GLM-4.5-Air | - | 32k | - | 64k |
| Qwen3.6 35B-A3B | 256k | 1M | 512k | 1M |
| Gemma 4 31B | 128k | 256k | 256k | 256k |
| Qwen3-Coder 30B-A3B | 128k | 512k | 128k | 1M |
| Qwen3.6 27B | 256k | 1M | 256k | 1M |
| Gemma 4 26B-A4B | 256k | 256k | 256k | 256k |
| Mistral Small 3.2 24B | 64k | 128k | 128k | 128k |
| gpt-oss 20B | 128k | 128k | 128k | 128k |
| Gemma 4 12B | 256k | 256k | 256k | 256k |
| Qwen3.5 9B | 1M | 1M | 1M | 1M |
| Qwen3-VL 8B | 128k | 512k | 256k | 1M |
| Qwen3.5 4B | 1M | 1M | 1M | 1M |
Spec summary
| 2x RTX 3090 (48GB total) | RTX PRO 6000 96GB (bonus page) | |
|---|---|---|
| Usable memory for model + KV cache | 46.0 GB | 95.0 GB |
| Memory bandwidth | 936 GB/s | 1792 GB/s |
| Models that fit fully at 16k | 11 of 13 | 13 of 13 |
| Models that need offload or do not fit | 2 | 0 |
| Measured data points on file | none (multi-GPU is estimated) | 6 |
FAQ
Which is faster for local LLMs, 2x RTX 3090 (48GB total) or RTX PRO 6000 96GB (bonus page)?
On the 11 models that fit fully on both, RTX PRO 6000 96GB (bonus page) is typically 1.3x faster (geometric mean). 2x RTX 3090 (48GB total) wins 4 and RTX PRO 6000 96GB (bonus page) wins 7. 0 comparisons are measured on both.
Which fits more models, 2x RTX 3090 (48GB total) or RTX PRO 6000 96GB (bonus page)?
2x RTX 3090 (48GB total) fits 11 of 13 models fully at 16k context and RTX PRO 6000 96GB (bonus page) fits 13. Only RTX PRO 6000 96GB (bonus page): gpt-oss 120B, GLM-4.5-Air.
Can I run long context on 2x RTX 3090 (48GB total) or RTX PRO 6000 96GB (bonus page)?
At f16 KV cache, the largest-model context limits are in the table above (for example Qwen3.6 35B-A3B on 2x RTX 3090 (48GB total): 256k; gpt-oss 120B on RTX PRO 6000 96GB (bonus page): 128k). A q8_0 KV cache extends it further.
Do you state prices for 2x RTX 3090 (48GB total) or RTX PRO 6000 96GB (bonus page)?
No. Prices change daily, so we link to Amazon search results instead and focus on measured and estimated speed and memory fit.
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2x RTX 3090 (48GB total) · RTX PRO 6000 96GB (bonus page)
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