RTX 4090 vs RTX A6000 for Local AI
1. 30-second verdict
This pair is capacity versus speed, not a same-class refresh. The RTX A6000 wins addressable VRAM and the calculated 70B fit: 48 GB GDDR6 discrete VRAM, 768 GB/s, 300 W, Ampere, launch MSRP $4,500. The RTX 4090 wins bandwidth, CUDA count, and the only same-lab 8B decode pair: 24 GB GDDR6X, 1008 GB/s, 16,384 CUDA cores vs 10,752, 450 W, Ada Lovelace, launch MSRP $1,599 (NVIDIA manufacturer specs). On hardware-corner.net llama.cpp Qwen3-8B Q4_K_XL (16K, batch 1, 2025-12-09) the 4090 recorded 104.31 tok/s vs 64.26 tok/s. Street price is not yet measured on the 4090; one Amazon new offer lists the A6000 at $6,190 (seen 2026-09-08).
2. Memory architecture
Both cards are discrete VRAM, not unified memory. Addressable GPU memory equals physical VRAM: 24 GB (4090) and 48 GB (A6000). Both use a 384-bit bus. The 4090 is GDDR6X at 1008 GB/s; the A6000 is GDDR6 at 768 GB/s. ECC is not recorded on either gpus row. The A6000 records nvlink_support=1 and 112.5 GB/s bidirectional NVLink bandwidth; the desktop 4090 records nvlink_support=0. Two 4090s stay 2×24 GB unless a runtime shards. Two NVLinked A6000s can present a larger pool; dual-card tok/s is still not yet measured here.
3. Model-fit table
Fit labels use ai_models calculated min/recommended configs plus discrete VRAM from the product records. They are capacity checks, not speed claims.
| Model (DB) | Calculated min (DB) | RTX 4090 24 GB | RTX A6000 48 GB |
|---|---|---|---|
| Llama 3.1 8B (8.03B) | 8 GB VRAM at Q4_K_M, 4k (calculated) | Fits | Fits |
| Qwen 3 8B | 8 GB VRAM at Q4_K_M, 4k | Fits | Fits |
| Mistral Small 3.2 24B | 20 GB at Q4_K_M, 4k; recommended 24 GB at Q6–Q8 | Fits Q4; Q6–Q8 is the recommended class | Fits Q4 and recommended Q6–Q8 class |
| Gemma 3 27B | 24 GB at Q4_K_M, 4k; recommended 32 GB+ for long context | Fits Q4 short context | Meets recommended 32 GB+ class |
| Qwen2.5 Coder 32B | 24 GB+ Q4_K_M; recommended 32 GB+ full context | Q4 short context only | Meets recommended 32 GB+ class |
| Llama 3.1 70B / Llama 3.3 70B | 48 GB at Q4_K_M, 4k (calculated; A6000 named as example) | Does not meet calculated min | Meets calculated min at 4k Q4 |
| FLUX.1 dev | 12 GB NF4/Q4 min; recommended 16 GB FP8 | Fits | Fits |
4. Direct benchmark table
Only non-quarantined benchmarks rows. Same-lab pairs first. A blank cell means no sourced row for that card — not zero. Estimate means is_estimate=1. Do not treat one-sided rows as a pair.
| Workload | Stack / notes | RTX 4090 | RTX A6000 | Class |
|---|---|---|---|---|
| Qwen3-8B Q4_K_XL gen | llama.cpp llama-bench CUDA 12.8, 16K, batch 1; hardware-corner.net; 2025-12-09; tier 3 | 104.31 tok/s | 64.26 tok/s | sourced same-lab |
| Llama-3-8B Q4_K_M gen | llama.cpp CUDA, 1024-token gen, batch 1, full offload; github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference; 2024-05-13; A6000 only (repo notes a 4090 cross-check of 127.74 tok/s that is not stored as a 4090 row here) | not in this DB row | 102.22 tok/s | sourced one-sided |
| Llama-3.1-8B Q4_K_M gen | llama.cpp b3500 CUDA, batch 1, 4k, mean of 3; myaihardware.com; 2026-05-22; 4090 only | 125 tok/s | no row | sourced one-sided |
| Llama-3.1-8B Q4_K_M prompt | same 4090 stack, 512-token prompt | 4800 tok/s | no row | sourced one-sided |
| UL Procyon Phi-3.5-mini | TensorRT, ThreadRipper 7980X, driver 571.86; StorageReview; 2025-01-29; 4090 only | 4958 score (244.343 tok/s noted) | no row | sourced one-sided |
| UL Procyon Mistral-7B | same lab; 4090 only | 5094 (183.266 tok/s noted) | no row | sourced one-sided |
| UL Procyon Llama-3-8B | same lab; 4090 only | 4849 (150.039 tok/s noted) | no row | sourced one-sided |
| UL Procyon Llama-2-13B | same lab; 4090 only | 5013 (92.853 tok/s noted) | no row | sourced one-sided |
| SDXL Turbo FP16 | ComfyUI; Tom's Hardware URL (4090) / Puget Systems labs URL (A6000); 2025-06-01; tier 2 — not same lab | 80 img/min | 35 img/min | sourced, different labs |
| UL Procyon SD 1.5 FP16 | StorageReview; 4090 only | 5260 (1.188 s/image) | no row | sourced one-sided |
| UL Procyon SD 1.5 INT8 | same lab; 4090 only | 62160 (0.503 s/image) | no row | sourced one-sided |
| UL Procyon SDXL FP16 | same lab; 4090 only | 5025 (7.461 s/image) | no row | sourced one-sided |
| Llama-3-70B Q4 gen | llama.cpp; Tom's Hardware (4090) / Puget Systems (A6000); 2025-06-01; notes say heavy swapping on 24 GB | 18 tok/s | 22 tok/s | estimate |
| Llama-3.1-8B FP16 batch | vLLM; Spheron blog; ~18 GB used; 4090 only | 2550 tok/s | no row | estimate |
| FLUX.1-dev BF16 | Diffusers; Spheron blog; 4090 only; notes say memory-efficient attention inside 24 GB | 4 img/min | no row | estimate |
5. Runtime compatibility
Both are NVIDIA CUDA discrete GPUs. Sourced stacks in our rows: llama.cpp CUDA, ComfyUI, plus 4090-only UL Procyon TensorRT, vLLM (estimate), and Diffusers (estimate). ROCm and MLX do not apply. Fine-tune / multi-adapter stacks beyond those rows are not yet measured on this pair.
6. Quantization compatibility
Sourced generation rows use Q4_K_M and Q4_K_XL on 8B models. Image rows use FP16 (and 4090-only UL Procyon INT8 plus an estimate BF16 FLUX). Q5/Q6/Q8 70B-class decode is not yet measured. Capacity still follows the model-fit table: 24 GB vs 48 GB changes which quant + context combination fits. The A6000 is the named 48 GB example for Llama 3.1 70B Q4_K_M at 4k; that is a fit claim, not a tok/s claim.
7. Max practical context examples
The same-lab llama.cpp pair uses 16K context (Qwen3-8B). The 4090-only Llama-3.1-8B row uses 4k context. Llama 3.1 8B native context in our model record is 131072 tokens; whether either GPU holds that window at a given quant is not yet measured here. For Llama 3.1 70B the calculated min is already 48 GB at 4k Q4_K_M, so long-context 70B on a single 24 GB card is outside that calculated fit; 32k on 70B raises the recorded requirement to about 57.3 GB and is outside a single A6000 as well.
8. Power
Specifications record 450 W TDP (4090) and 300 W TDP (A6000). Sustained inference holds near those ratings. Plan PSU and case airflow for the 4090's higher board power; the A6000 is the cooler of the two on paper. Energy cost per million tokens is not yet measured (no watt-hour rows).
9. Current price
Price class is launch_msrp plus one A6000 offer. Products table: RTX 4090 $1,599, RTX A6000 $4,500, price_checked_at 2026-09-02. street_price_usd is NULL on both. One product_offers row: Amazon new A6000 $6,190, seen 2026-09-08, in stock. No 4090 offer row. Check live listings rather than treating MSRP as street:
- RTX 4090 current listing (affiliate)
- RTX A6000 current listing (affiliate)
- RTX 4090 product page · RTX A6000 product page
10. Used price
Not yet measured. No used-condition offer rows exist for either SKU. A used 4090 can still be the right speed buy on 24 GB workloads (see the used RTX 4090 buying guide), but this page will not invent a median. Used A6000 street is likewise unknown here.
11. Performance per current dollar
Not computed. Mixing launch MSRP, one $6,190 A6000 offer, and tok/s invents a ranking the database does not store. Use the sourced table plus the price you actually pay.
12. Multi-GPU considerations
Desktop RTX 4090 records nvlink_support = 0. Two 4090s are 2×24 GB, not 48 GB of one pool, unless the runtime shards (tensor parallel / pipeline / expert offload). RTX A6000 records nvlink_support = 1 and 112.5 GB/s bidirectional. Dual-card tok/s for this pair is not yet measured. Related: multi-GPU guide, workstations.
13. System requirements
Both: PCIe 4.0 x16. 4090: 450 W TDP, consumer GeForce (product category Consumer GPU). A6000: 300 W TDP, workstation RTX Pro (product category Pro GPU). For single-GPU inference the interface generation is the same; VRAM and bandwidth dominate. Both need a case and cooler rated for a full-height workstation / flagship card and a PSU with headroom above TDP. CPU/RAM pairing for these two SKUs is not yet measured on this page.
14. Who should buy the RTX 4090
- 8B–24B Q4 work that already fits in 24 GB, where sourced 8B decode and SDXL-Turbo speed matter more than 48 GB.
- Buyers who want Ada / 1008 GB/s / 16,384 CUDA cores at $1,599 launch MSRP and accept 450 W plus no NVLink.
- Not a substitute for a 48 GB card when the model-fit min is 48 GB (Llama 3.1 / 3.3 70B Q4_K_M at 4k).
15. Who should buy the RTX A6000
- Workloads whose calculated min is 48 GB, including Llama 3.1 / 3.3 70B Q4_K_M at 4k (A6000 is the named example in that model record).
- Buyers who need recorded NVLink and 300 W TDP more than Ada bandwidth, and who accept $4,500 launch MSRP (or the $6,190 Amazon new offer).
- Anyone who would otherwise offload 70B layers on a 24 GB card. Dual-A6000 tok/s is still not yet measured here.
16. Evidence / source table
| Claim | Value | Source class | Origin |
|---|---|---|---|
| VRAM / type / bus / bandwidth / TDP / CUDA / PCIe / arch | 24 GB GDDR6X 384-bit 1008 GB/s 450 W 16384 PCIe 4.0 Ada vs 48 GB GDDR6 384-bit 768 GB/s 300 W 10752 PCIe 4.0 Ampere | manufacturer_spec (confidence 0.95) | specifications + gpus; NVIDIA 40-series / RTX A6000 pages on product source_url |
| Launch MSRP | $1599 / $4500 | launch_msrp | products.msrp_usd; price_checked_at 2026-09-02 |
| Street / used | 4090 unknown; A6000 Amazon new $6190 | unknown / product_offers | street_price_usd NULL; offer id 34 seen 2026-09-08 |
| Qwen3-8B llama.cpp pair | 104.31 vs 64.26 tok/s | benchmark tier 3, not estimate | https://www.hardware-corner.net/gpu-ranking-local-llm/ |
| Llama-3-8B A6000 only | 102.22 tok/s | benchmark tier 3, not estimate | https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference |
| 4090 llama.cpp 8B + UL Procyon | scores in §4 | benchmark tier 3 / 2, not estimate | https://www.myaihardware.com/llama-cpp-benchmarks/ ; https://www.storagereview.com/review/nvidia-geforce-rtx-5080-review-the-sweet-spot-for-ai-workloads |
| SDXL Turbo ComfyUI | 80 vs 35 img/min | benchmark tier 2, not estimate, different labs | https://www.tomshardware.com/pc-components/gpus ; https://www.pugetsystems.com/labs/ |
| Llama-3-70B Q4; vLLM 8B; FLUX BF16 | 18 vs 22 tok/s; 2550 tok/s 4090-only; 4 img/min 4090-only | estimate (is_estimate=1) | Tom's Hardware / Puget / Spheron URLs on those rows |
| 70B calculated fit | 48 GB min Q4_K_M 4k | calculated model-fit | ai_models.llama-3-1-70b / llama-3-3-70b min_config |
| NVLink | 4090 unsupported; A6000 112.5 GB/s bidirectional | gpus.nvlink_support / nvlink_bandwidth | gpus table, last_verified_at 2026-09-02 |
17. Data freshness date
21 September 2026. Product specs last verified in gpus.last_verified_at / products.price_checked_at on 2026-09-02. Newest sourced bench date in this pair: llama.cpp 8B on the 4090 2026-05-22. Same-lab pair date: 2025-12-09. A6000 Amazon offer seen 2026-09-08. This page does not invent newer street prices or unsourced tok/s.
Frequently asked questions
Is the RTX A6000 better than the RTX 4090 for AI?
For capacity, yes: our product records list 48 GB GDDR6 on the RTX A6000 versus 24 GB GDDR6X on the RTX 4090, and the A6000 is the named 48 GB example in the Llama 3.1 70B Q4_K_M / 4k calculated min. For sourced 8B decode speed, the 4090 is faster on the same-lab Qwen3-8B llama.cpp pair (104.31 tok/s vs 64.26 tok/s). Pick capacity or speed; this page does not invent a single winner score.
Can the RTX 4090 run Llama 3.1 70B?
Our model-fit record for Llama 3.1 70B lists a calculated minimum of 48 GB VRAM at Q4_K_M / 4k context and names the RTX A6000 as an example of that class. A single 24 GB RTX 4090 does not meet that calculated min. A sourced estimate row (is_estimate=1) still lists Llama-3-70B Q4 at 18 tok/s on the 4090 with heavy swapping and 22 tok/s on the A6000 — treat those as estimates, not measured same-lab pairs.
Which card uses more power?
The RTX 4090. Specifications record 450 W TDP for the RTX 4090 and 300 W TDP for the RTX A6000 (manufacturer datasheet).
Which GPU is faster for local LLM inference?
On the same-lab llama.cpp Qwen3-8B Q4_K_XL pair (hardware-corner.net, 16K context, batch 1, 2025-12-09), the RTX 4090 recorded 104.31 tok/s versus 64.26 tok/s on the RTX A6000. Other 8B rows exist for one card only and are not same-lab pairs. Larger-model speeds beyond those rows are not-yet-measured or labeled estimates.
Does the RTX A6000 have NVLink?
Yes. Our gpus table records nvlink_support=1 and nvlink_bandwidth=112.5 GB/s bidirectional for the RTX A6000. The desktop RTX 4090 records nvlink_support=0. Dual-card tok/s for either pairing is not yet measured here.
What do these cards cost?
Launch MSRP in the products table is $1,599 (RTX 4090) and $4,500 (RTX A6000), last price-checked 2026-09-02. Street price is NULL on both. One Amazon new offer row exists for the A6000 at $6,190 seen 2026-09-08. The 4090 has no product_offers row. Used prices are not yet measured.
Related: best GPU for local LLMs, best GPU for Stable Diffusion, RTX 5090 vs 4090, RTX 5080 vs 4090, used RTX 4090 guide, RTX PRO 6000 Blackwell, GPU index, Can it run?, GPU comparison tool.