RTX 5090 vs RTX 4090 for Local AI
1. 30-second verdict
The RTX 5090 wins local AI capacity and sourced single-GPU throughput: 32 GB GDDR7 discrete VRAM and 1792 GB/s versus 24 GB GDDR6X and 1008 GB/s on the RTX 4090 (NVIDIA manufacturer specs). On the same-lab llama.cpp Llama-3.1-8B Q4_K_M pair, the 5090 recorded 215 tok/s vs 125 tok/s. The RTX 4090 wins launch MSRP and board power: $1,599 / 450 W versus $1,999 / 575 W. Street and used prices are not yet measured (no product_offers rows). Neither single card meets our calculated 48 GB minimum for Llama 3.1 70B Q4_K_M at 4k context.
2. Memory architecture
Both cards are discrete VRAM, not unified memory. Addressable GPU memory equals physical VRAM: 32 GB (5090) and 24 GB (4090). The 5090 uses GDDR7 on a 512-bit bus; the 4090 uses GDDR6X on a 384-bit bus. Do not treat either figure as Apple/AMD unified RAM. NVLink is recorded as unsupported on both desktop SKUs, so two cards do not become one 48 GB or 64 GB pool unless a runtime explicitly shards or offloads.
3. Model-fit table
Fit labels below use our 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 5090 32 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 tight | Fits Q4; more Q6–Q8 headroom |
| Gemma 3 27B | 24 GB at Q4_K_M, 4k; recommended 32 GB+ for long context | Fits Q4 short context | Matches recommended 32 GB+ class for longer context |
| Qwen2.5 Coder 32B | 24 GB+ Q4_K_M; recommended 32 GB+ full context | Q4 short context only | Matches recommended 32 GB+ class |
| Llama 3.1 70B | 48 GB at Q4_K_M, 4k (calculated) | Does not meet calculated min | Does not meet calculated min |
| 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. Estimate means is_estimate=1. Community-unverified means is_estimate=0 but evidence_type=community_unverified with a confidence score below 1.0 — directional, not lab-grade.
| Workload | Stack / notes | RTX 5090 | RTX 4090 | Class |
|---|---|---|---|---|
| Llama-3.1-8B Q4_K_M gen | llama.cpp b3500 CUDA, batch 1, 4k, mean of 3; myaihardware.com; 2026-05-22; tier 3 | 215 tok/s | 125 tok/s | sourced |
| Llama-3.1-8B Q4_K_M prompt | same stack, 512-token prompt | 9800 tok/s | 4800 tok/s | sourced |
| Qwen3-8B Q4_K_XL gen | llama.cpp llama-bench CUDA 12.8, 16K, batch 1; hardware-corner.net; 2025-12-09; tier 3 | 145.34 tok/s | 104.31 tok/s | sourced |
| Qwen3.8-27B gen | llama.cpp (server), --parallel 1, spec-off baseline; single-source A/B repo table; github.com/sudoingX/qwen38-mtp; tested 2026-09-17; tier 3 | 61.4 tok/s | 47.7 tok/s | community-unverified (confidence 0.55) |
| UL Procyon Phi-3.5-mini | TensorRT, ThreadRipper 7980X, driver 571.86; StorageReview; 2025-01-29; tier 2 | 5749 score (314.435 tok/s noted) | 4958 score (244.343 tok/s noted) | sourced |
| UL Procyon Mistral-7B | same lab | 6267 (255.945 tok/s noted) | 5094 (183.266 tok/s noted) | sourced |
| UL Procyon Llama-3-8B | same lab | 6104 (214.285 tok/s noted) | 4849 (150.039 tok/s noted) | sourced |
| UL Procyon Llama-2-13B | same lab | 6591 (134.502 tok/s noted) | 5013 (92.853 tok/s noted) | sourced |
| SDXL Turbo FP16 | ComfyUI; Tom's Hardware URL; 2025-06-01; tier 2 | 120 img/min | 80 img/min | sourced |
| UL Procyon SD 1.5 FP16 | StorageReview same lab | 8193 (0.763 s/image) | 5260 (1.188 s/image) | sourced |
| UL Procyon SD 1.5 INT8 | same lab | 79272 (0.394 s/image) | 62160 (0.503 s/image) | sourced |
| UL Procyon SDXL FP16 | same lab | 7179 (5.223 s/image) | 5025 (7.461 s/image) | sourced |
| Llama-3-70B Q4 gen | llama.cpp; Tom's Hardware; 2025-06-01; notes say tight fit / heavy swapping | 42 tok/s | 18 tok/s | estimate |
| Llama-3.1-8B FP16 batch | vLLM; Spheron blog; ~18 GB used | 3500 tok/s | 2550 tok/s | estimate |
| FLUX.1-dev BF16 | Diffusers; Spheron blog | 5.5 img/min | 4 img/min | estimate |
5. Runtime compatibility
Both are NVIDIA CUDA discrete GPUs. Sourced stacks in our rows: llama.cpp CUDA, vLLM (estimate rows), Diffusers (estimate), ComfyUI, and UL Procyon TensorRT. 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, plus UL Procyon default (unspecified quant) TensorRT text scores. Image rows use FP16, INT8, and an estimate BF16 FLUX pair. Q5/Q6/Q8 70B-class decode on either card is not yet measured. Capacity still follows the model-fit table: 24 GB vs 32 GB changes which quant + context combination fits, not which runtime exists.
7. Max practical context examples
Same-lab llama.cpp rows use 4k context (Llama-3.1-8B) and 16K context (Qwen3-8B). 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 or 32 GB card is outside that calculated fit.
8. Power
Specifications record 575 W TDP (5090) and 450 W TDP (4090). Sustained inference holds near those ratings. Plan PSU, 12V-2x6 / 12VHPWR cabling, and case airflow for flagship board power. Energy cost per million tokens is not yet measured (no watt-hour rows).
9. Current price
Price class is launch_msrp only. Products table: RTX 5090 $1,999, RTX 4090 $1,599, price_checked_at 2026-09-02. street_price_usd is NULL on both. No product_offers rows. Check live listings rather than treating MSRP as street:
- RTX 5090 current listing (affiliate)
- RTX 4090 current listing (affiliate)
- RTX 5090 product page · RTX 4090 product page
10. Used price
Not yet measured. No used-condition offer rows exist for either SKU. A used 4090 can be the right buy on capacity grounds (see the used RTX 4090 buying guide), but this page will not invent a median.
11. Performance per current dollar
Not computed. Street price is unknown, and mixing launch MSRP with tok/s invents a value ranking the database does not store. Use the sourced table plus the price you actually pay.
12. Multi-GPU considerations
Both desktop SKUs record nvlink_support = 0. Two 4090s are 2×24 GB, not 48 GB of one pool, unless the runtime shards (tensor parallel / pipeline / expert offload). Two 5090s are 2×32 GB under the same rule. Dual-card tok/s for this pair is not yet measured. Related system pages: workstations, custom 5090 builds, dual-4090 training boxes in the catalog.
13. System requirements
5090: PCIe 5.0 x16, 575 W TDP. 4090: PCIe 4.0 x16, 450 W TDP. For single-GPU inference the interface generation matters less than VRAM and bandwidth. Both need a case and cooler rated for a 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 5090
- Workloads that use the extra 8 GB (27B–32B at higher quant or longer context; image pipelines that sit near 24 GB).
- Buyers who want the sourced 8B llama.cpp and UL Procyon gaps above, and accept 575 W plus $1,999 launch MSRP.
- Not a substitute for a 48 GB+ pro card when the model-fit min is 48 GB (Llama 3.1 70B Q4_K_M at 4k).
15. Who should buy the RTX 4090
- 8B–24B Q4 work that already fits in 24 GB, where $1,599 launch MSRP and 450 W matter more than 32 GB / 1792 GB/s.
- Image generation that fits the 24 GB FLUX/SDXL class in the model-fit table.
- Anyone waiting on a measured used-market price — still unknown here — before paying 5090 launch MSRP.
16. Evidence / source table
| Claim | Value | Source class | Origin |
|---|---|---|---|
| VRAM / type / bus / bandwidth / TDP / CUDA / PCIe / arch | 32 GB GDDR7 512-bit 1792 GB/s 575 W 21760 PCIe 5.0 Blackwell vs 24 GB GDDR6X 384-bit 1008 GB/s 450 W 16384 PCIe 4.0 Ada | manufacturer_spec (confidence 0.95) | specifications + gpus; NVIDIA 50-series / 40-series pages on product source_url |
| Launch MSRP | $1999 / $1599 | launch_msrp | products.msrp_usd; price_checked_at 2026-09-02 |
| Street / used | not yet measured | unknown | street_price_usd NULL; product_offers empty |
| Llama-3.1-8B llama.cpp pair | 215 vs 125 tok/s | benchmark tier 3, not estimate | https://www.myaihardware.com/llama-cpp-benchmarks/ |
| Qwen3-8B llama.cpp pair | 145.34 vs 104.31 tok/s | benchmark tier 3, not estimate | https://www.hardware-corner.net/gpu-ranking-local-llm/ |
| UL Procyon text + image | scores in §4 | benchmark tier 2, not estimate | https://www.storagereview.com/review/nvidia-geforce-rtx-5080-review-the-sweet-spot-for-ai-workloads |
| SDXL Turbo ComfyUI | 120 vs 80 img/min | benchmark tier 2, not estimate | https://www.tomshardware.com/pc-components/gpus |
| Llama-3-70B Q4; vLLM 8B; FLUX BF16 | 42 vs 18 tok/s; 3500 vs 2550; 5.5 vs 4 img/min | estimate (is_estimate=1) | Tom's Hardware / Spheron blog URLs on those rows |
| 70B calculated fit | 48 GB min Q4_K_M 4k | calculated model-fit | ai_models.llama-3-1-70b min_config |
| NVLink | unsupported both | gpus.nvlink_support=0 | 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 2026-05-22. UL Procyon lab date: 2025-01-29. This refresh does not invent newer street prices or unsourced tok/s.
Frequently asked questions
Is the RTX 5090 worth it over the RTX 4090 for AI?
If you need 32 GB of discrete VRAM or the extra 1792 GB/s of bandwidth, yes: those are NVIDIA manufacturer specs in our product records. If your models fit in 24 GB, the RTX 4090 at a $1,599 launch MSRP is the cheaper card. Street price is not-yet-measured in our offers table.
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. Neither the 24 GB RTX 4090 nor the 32 GB RTX 5090 meets that calculated minimum as a single card. A sourced estimate row (Tom's Hardware, flagged is_estimate=1) still lists Llama-3-70B Q4 at 18 tok/s on the 4090 with heavy swapping and 42 tok/s on the 5090 with a tight fit — treat those as estimates, not measured same-lab pairs.
Does the RTX 5090 use more power than the RTX 4090?
Yes. Our specifications table records 575 W TDP for the RTX 5090 and 450 W TDP for the RTX 4090 (manufacturer datasheet).
Which GPU is faster for local LLM inference?
On the same-lab llama.cpp Llama-3.1-8B Q4_K_M pair (myaihardware.com, 4k context, batch 1), the RTX 5090 recorded 215 tok/s versus 125 tok/s on the RTX 4090. On the same-lab Qwen3-8B Q4_K_XL pair (hardware-corner.net, 16K context), it is 145.34 tok/s versus 104.31 tok/s. Larger-model speeds beyond those rows are not-yet-measured or labeled estimates.
Do both cards have NVLink?
No. Our gpus table records nvlink_support=0 for both desktop RTX 5090 and RTX 4090. Multi-GPU on these cards is PCIe peer-to-peer / runtime-dependent, not NVLink.
What is the used price of an RTX 4090?
Not yet measured. Our product_offers table has no used or street rows for either card. Launch MSRP in the products table is $1,599 (4090) and $1,999 (5090), last price-checked 2026-09-02.
Related: best GPU for local LLMs, best GPU for Stable Diffusion, RTX 5090 vs 5080, RTX 5080 vs 4090, RTX 5090 vs RX 7900 XTX, used RTX 4090 guide, GPU index, Can it run?.