Home / Compare / NVIDIA RTX A6000 48 GB vs NVIDIA RTX 3090 24 GB
NVIDIA RTX A6000 48 GB vs NVIDIA RTX 3090 24 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 3090 24 GB | |
|---|---|---|
| Advertised VRAM | 48 GB | 24 GB |
| Usable VRAM (calculator 90%) | 43.2 GB | 21.6 GB |
| Architecture | Ampere | Ampere |
| Memory | GDDR6 · 768 GB/s · 384-bit | GDDR6X · 936.2 GB/s · 384-bit |
| Models fully fitting at Q4 / 8K | 72 | 62 |
| Limited context / offload / does not fit | 0 / 8 / 2 | 1 / 14 / 5 |
| llama.cpp short-context coverage | available | available |
| Mapped / Amazon-matched SKUs | 2 / 0 | 18 / 9 |
| Lowest fresh matched Amazon card | Check availability | $1347.22 Check price |
| Current price per advertised VRAM GB | — | $56 / 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.
Code Llama 7B Instruct at Q4 / 8K
| NVIDIA RTX A6000 48 GB | NVIDIA RTX 3090 24 GB | |
|---|---|---|
| Memory status | Fits Fits in VRAM | Fits Fits in VRAM |
| Required VRAM | 9.0 GB | 9.0 GB |
| Usable VRAM | 43.2 GB | 21.6 GB |
| VRAM headroom | 34.2 GB | 12.6 GB |
| Largest full-VRAM context in cache | 16K Memory-fit only — not a speed claim at that context | 16K 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 3090 24 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 (10)
Fits fully only on NVIDIA RTX 3090 24 GB (0)
None
62 models fit fully on both · examples: Qwen3 30B-A3B, Qwen3-Coder 30B-A3B Instruct, Gemma 2 27B Instruct, Gemma 4 26B-A4B Instruct, Mistral Small 3.2 24B Instruct, Mistral Small 24B Instruct, Magistral Small, GPT-OSS 20B.
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 3090 24 GB | |
|---|---|---|
| Decode | ~130 tok/s · Calibrated estimate · HIGH confidence | ~146 tok/s · Calibrated estimate · HIGH confidence |
| Prefill (pp512) | ~5662 tok/s · MEDIUM | ~5560 tok/s · MEDIUM |
For this llama.cpp short-context workload, the current estimates are ~130 tok/s vs ~146 tok/s. About ~0.89× in this specific modeled workload.
What this comparison shows
- Memory capacity: NVIDIA RTX A6000 48 GB has 24 GB more advertised VRAM.
- Model fit: at Q4 / 8K, NVIDIA RTX A6000 48 GB fully fits 10 additional supported models.
- Selected workload: both GPUs are FITS IN VRAM for Code Llama 7B Instruct at Q4 / 8192 tokens.
- Performance: for the supported llama.cpp short-context workload, NVIDIA RTX A6000 48 GB ~130 tok/s Calibrated estimate HIGH; NVIDIA RTX 3090 24 GB ~146 tok/s Calibrated estimate HIGH.
- 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 Code Llama 7B Instruct · See GPUs under a budget · Value explorer