Home / Compare / NVIDIA RTX A6000 48 GB vs NVIDIA RTX 5090 32 GB
NVIDIA RTX A6000 48 GB vs NVIDIA RTX 5090 32 GB for local AI
Canonical GPU chips, not ASUS vs MSI coolers. Memory fit uses calculator v1.0.0. Speed uses published llama.cpp estimates only. Query states (model / quant / context) are not indexed.
Decision dimensions
These are facts from the existing memory calculator, performance cache, and Amazon summary. This page does not pick a winner.
| NVIDIA RTX A6000 48 GB | NVIDIA RTX 5090 32 GB | |
|---|---|---|
| Advertised VRAM | 48 GB | 32 GB |
| Usable VRAM (calculator 90%) | 43.2 GB | 28.8 GB |
| Architecture | Ampere | Blackwell |
| Memory | GDDR6 · 768 GB/s · 384-bit | GDDR7 · 1792 GB/s · 512-bit |
| Models fully fitting at Q4 / 8K | 72 | 71 |
| Limited context / offload / does not fit | 0 / 8 / 2 | 0 / 8 / 3 |
| llama.cpp short-context coverage | available | available |
| Mapped / Amazon-matched SKUs | 2 / 0 | 8 / 8 |
| Lowest fresh matched Amazon card | Check availability | $4699.99 Check price |
| Current price per advertised VRAM GB | — | $147 / GB |
Prices are the lowest fresh Amazon-matched board-partner SKU in the last 24 hours, not MSRP. Price per GB is that price divided by advertised VRAM — not a value score.
Workload
Runtime is llama.cpp CUDA — the only runtime with published Phase 5 estimates. Changing the model does not create a new indexable URL.
Phi-4 Mini Reasoning at Q4 / 8K
| NVIDIA RTX A6000 48 GB | NVIDIA RTX 5090 32 GB | |
|---|---|---|
| Memory status | Fits Fits in VRAM | Fits Fits in VRAM |
| Required VRAM | 4.2 GB | 4.2 GB |
| Usable VRAM | 43.2 GB | 28.8 GB |
| VRAM headroom | 39.0 GB | 24.6 GB |
| Largest full-VRAM context in cache | 128K Memory-fit only — not a speed claim at that context | 128K Memory-fit only — not a speed claim at that context |
Can it run on NVIDIA RTX A6000 48 GB? · Can it run on NVIDIA RTX 5090 32 GB?
What each GPU uniquely fits
Full VRAM fit at Q4 / 8K among published calculation-supported models. Identical coverage is reported as such — it is not turned into a winner.
Fits fully only on NVIDIA RTX A6000 48 GB (1)
Fits fully only on NVIDIA RTX 5090 32 GB (0)
None
71 models fit fully on both · examples: Code Llama 34B Instruct, DeepSeek Coder 33B Instruct, Qwen3 32B, DeepSeek R1 Distill Qwen 32B, Qwen2.5 32B Instruct, Qwen2.5-Coder 32B Instruct, QwQ 32B, Gemma 4 31B Instruct.
llama.cpp performance
Reuses Performance Model v1.0.0 published rows only. Short-context llama-bench pp512/tg128. Not 8K–128K speed. Methodology
| NVIDIA RTX A6000 48 GB | NVIDIA RTX 5090 32 GB | |
|---|---|---|
| Decode | No published llama.cpp performance estimate for this GPU. | No published llama.cpp performance estimate for this GPU. |
| Prefill (pp512) | unavailable | unavailable |
No published llama.cpp performance estimate for either GPU.
What this comparison shows
- Memory capacity: NVIDIA RTX A6000 48 GB has 16 GB more advertised VRAM.
- Model fit: at Q4 / 8K, NVIDIA RTX A6000 48 GB fully fits 1 additional supported model.
- Selected workload: both GPUs are FITS IN VRAM for Phi-4 Mini Reasoning at Q4 / 8192 tokens.
- Performance: No published llama.cpp performance estimate for either GPU.
- Current commerce: no fresh Amazon-matched price for NVIDIA RTX A6000 48 GB.
Board power / TDP is not compared: RigForAI does not yet have a single normalized watt metric. Energy per token is out of scope. This is not a best-GPU ranking.
See cheapest currently buyable GPUs for Phi-4 Mini Reasoning · See GPUs under a budget · Value explorer