NVIDIA GeForce RTX 5090
Blackwell flagship. 32GB GDDR7 on a 512-bit bus delivers ~1.79 TB/s memory bandwidth — the new top of consumer hardware for local LLM inference. Comfortably loads 70B Q4 with room for context.
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Sub-scores sum to 900 / 1000. Headline = 900 × 0.70 (Estimated-confidence discount) = 630. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 1792 GB/s bandwidth — 215.0 tok/s estimated. No measured benchmarks yet.
Plain-English: Comfortable at 32B and below — snappy enough for a coding agent; vision models supported.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
Our verdict
What it does well
The 32 GB VRAM is the operational headline — it's the smallest amount of memory that runs the 70B-class models fully on-GPU at Q4 (Llama 3.3 70B, DeepSeek R1 Distill 70B, Qwen 2.5 72B all land in 39–42 GB with KV cache headroom for 8K context). Memory bandwidth at ~1.79 TB/s is the second headline — that's roughly 1.8× the RTX 4090's 1.0 TB/s, and decode speed scales nearly linearly with bandwidth on memory-bound workloads, so 70B Q4 runs at 40–55 tok/s on a 5090 versus 22–28 tok/s on a 4090 with system-RAM offload. CUDA support is universal: every local runtime (vLLM, llama.cpp, Ollama, SGLang) has a happy path on consumer Blackwell.
Where it breaks
- 575 W TGP is real. This is a 1000 W+ PSU card, not a 750 W card. Add headroom for CPU + drives + transient spikes; many operators end up at 1200 W. The 12V-2x6 connector replaces the controversial 4090-era 12VHPWR but the fitment + power-budget caution stays.
- Supply + price are not normal yet. 2025-into-2026 retail is supply-constrained — MSRP $1,999 is rarely the price you actually pay. Scalper-adjacent pricing of $2,300–2,800 is the operator-grade reality.
- 32B-class workloads are over-spec. If your daily target is Qwen 3 32B / Qwen 2.5 Coder 32B / QwQ 32B, the 5090 isn't doing more for you than a 4090 does — the workload fits 24 GB. You're paying the 5090 premium for headroom you don't need.
- Multi-GPU economics are awkward. Two 5090s for ~$5,000 buy you 64 GB combined VRAM. Two used 3090s buy you 48 GB combined for ~$1,800. For homelab operators chasing $/VRAM, the calculus often favors the older silicon.
Ideal model range
- Sweet spot: 70B-class at Q4 full-GPU — Llama 3.3 70B, DeepSeek R1 Distill 70B, Qwen 2.5 72B at 40–55 tok/s with comfortable 8–16K context.
- Stretch: 70B at Q5_K_M (50 GB) — partial offload to system RAM, drops to 28–38 tok/s. Or 32B FP16 (64 GB) — same partial-offload story.
- Comfortable: 32B-class at Q4/Q8 with full 32K context, or 14B-class with 128K context, both at 80+ tok/s with significant headroom.
- Future-proof zone: emerging 32B reasoning models (R1-class) with extended thinking-token budgets fit comfortably; agent loops with 32–64K live context don't pressure VRAM.
Bad use cases
- Genuine frontier-MoE workloads — DeepSeek V3 671B, Llama 4 Maverick / Behemoth — need workstation hardware (RTX 6000 Ada / RTX PRO 6000 Blackwell) or multi-GPU. 32 GB doesn't change that math.
- Power-constrained builds — mini-ITX cases, 750 W PSUs, anyone running 24/7 inference and paying retail electricity. The 5090 is a thermal + power statement.
- Maximum tok/s on small models — 7B at >300 tok/s is throughput territory where smaller cards (RTX 5070 Ti, RTX 4070 Super) are better $/throughput. The 5090 is over-specced for sub-13B workloads.
- Anyone betting on future supply normalization — if the 5090 is still scalper-priced when you check, the 4090 used market and the dual-3090 path are honest alternatives. Don't pay 30%+ premium for a card you can wait on.
Verdict
Buy this if 70B Q4 fully on GPU is your daily-driver target, you can find one at $2,300 or below (so within a reasonable scalper premium), AND your build has 1000 W+ PSU + thermal headroom + a case that fits a 4-slot card. The 32 GB at 1.79 TB/s is genuinely the new sweet spot for serious local-AI work in 2026.
Skip this if the RTX 4090 covers your model range (32B-class), if used RTX 3090s make better $/VRAM sense for a multi-GPU rig, if your power envelope is tight, or if you're price-sensitive enough that a 30%+ scalper premium hurts. The 5090 isn't a value play; it's a capability play.
How it compares
- vs RTX 4090 → 4090's 24 GB caps at 32B-class full-GPU and forces partial offload on 70B (22–28 tok/s vs 5090's 40–55 tok/s). Pick 5090 when 70B is the target, or you can wait for normal pricing. See /compare/hardware/rtx-4090-vs-rtx-5090.
- vs Dual RTX 3090 → 48 GB combined VRAM at ~$1,800 used vs $2,500 new. Better $/VRAM but real multi-GPU complexity (NCCL, driver pinning, PCIe lane budgeting). See /compare/hardware/dual-3090-vs-rtx-5090.
- vs Apple M4 Max 128 GB → unified memory comfortably runs 70B FP16 (~140 GB) where the 5090 can't. Apple wins on memory ceiling + total system noise/power; 5090 wins on raw decode speed + CUDA ecosystem maturity. See /compare/hardware/apple-m4-max-vs-rtx-5090.
- vs RTX 5080 (16 GB) → wrong tier — 5080 caps at 13B-class full-GPU. The 16 GB → 32 GB jump is the whole reason to pay the 5090 premium.
- vs RX 7900 XTX → 7900 XTX matches 24 GB at half the price but ROCm software stack still trails NVIDIA. Pick 5090 for production-grade local AI; 7900 XTX for hobby + Linux + tight budget.
- vs RTX 6000 Ada / RTX PRO 6000 Blackwell → 48–96 GB workstation VRAM at $7,000–$10,000. Right answer when you need >32 GB and can pay; the 5090 is the consumer ceiling, not the absolute ceiling.
Overview
What the RTX 5090 actually is, in local-AI terms
The RTX 5090 is the new consumer-flagship local-AI GPU in 2026 — 32 GB of GDDR7 at ~1.79 TB/s memory bandwidth, the Blackwell consumer architecture with native FP4 acceleration, and the first single consumer card with enough VRAM to host a 70B-class model at INT4 with comfortable context headroom on one PCIe slot. It is roughly 1.5-1.8× faster than the RTX 4090 on most LLM workloads and adds the architectural piece — FP4 — that consumer cards have lacked since the H100 introduced FP8 on Hopper.
It is also expensive, power-hungry, and supply-constrained through most of 2026. For operators who do not need 32 GB and do not need FP4, the 4090 still wins on dollars-per-token. For operators who do need either, there's no alternative below the RTX A6000 or RTX Pro 6000 Blackwell in the consumer-adjacent tier.
Where it fits in the hardware ladder
In the consumer-NVIDIA tier:
| Card | VRAM | BW | Bin |
|---|---|---|---|
| RTX 4090 | 24 GB | 1008 GB/s | workstation default through 2025 |
| RTX 5090 | 32 GB | 1792 GB/s | consumer flagship 2026 |
| RTX Pro 6000 Blackwell | 96 GB | ~1.8 TB/s | workstation tier above 5090 |
vs the datacenter ladder:
| Card | VRAM | BW | Notes |
|---|---|---|---|
| RTX 5090 | 32 GB | 1.79 TB/s | consumer; no NVLink |
| H100 SXM | 80 GB | 3.35 TB/s | datacenter; NVLink |
| H200 | 141 GB | 4.8 TB/s | datacenter capacity tier |
The 5090's 32 GB ceiling is what defines the 2026 "consumer-tier sweet spot" — large enough that 70B at INT4 fits comfortably, small enough that 405B is firmly out of reach without a multi-card or datacenter step.
Best use cases
- Single-card 70B-class inference. Llama 3.3 70B at AWQ-INT4 fits with realistic context headroom on a single 5090 — the first time this has been true on a consumer card. Pair with vLLM or ExLlamaV2.
- High-throughput single-user agentic stacks. Qwen 2.5 Coder 32B at FP16 fits with substantial context; a 4090 can't do that. See /stacks/local-coding-agent.
- FP4 inference experimentation. Blackwell consumer cards expose FP4 acceleration; the engines that target it (TensorRT-LLM, vLLM) are catching up through 2026.
- Local fine-tuning of 13B-32B models with QLoRA via PyTorch + bitsandbytes; 32 GB is enough to hold a quantized 32B + optimizer states + a meaningful batch.
- Concurrent image-gen + LLM. A 5090 can host a Stable Diffusion XL-class model and a 7B-13B chat model simultaneously without thrashing.
What it can run
The realistic working set on a single 5090 in May 2026:
| Model class | Quant | Context | Headroom |
|---|---|---|---|
| 7B | F16 | 128K | massive |
| 13B-14B | F16 | 64K | comfortable |
| 32B | F16 | 32K | comfortable |
| 32B | AWQ-INT4 | 128K | substantial |
| 70B | AWQ-INT4 / EXL2 4.0bpw | 16-32K | tight but works |
| 70B | FP4 (when engine-supported) | 32K | comfortable |
| 405B | — | — | does NOT fit single card |
For 405B-class you need a datacenter tier — see NVIDIA H100 SXM and /stacks/h100-tensor-parallel-workstation.
OS support
| OS | Quality |
|---|---|
| Linux (Ubuntu 24.04 LTS) | excellent — reference |
| Windows 11 native | excellent |
| Windows (WSL2) | excellent |
| macOS | unsupported |
If your CUDA path is broken on WSL2, see /errors/wsl2-gpu-not-detected.
Software / runtime support
The 5090's Blackwell architecture is supported across the leading-edge inference engines, with the caveat that engine support for FP4 lags hardware availability through 2026:
- Ollama / llama.cpp — full GGUF + CUDA; FP4 lands incrementally
- vLLM — full AWQ / GPTQ / FP16 / FP8; FP4 maturing through 2026
- SGLang — same coverage as vLLM
- ExLlamaV2 — single-stream throughput king on this hardware via TabbyAPI
- TensorRT-LLM — first-class; FP4 path the most mature here
- LM Studio — full GUI path with CUDA acceleration
- PyTorch — first-class CUDA target
What breaks first
- Power delivery. The 5090 pulls up to 575 W under sustained inference; the 12V-2x6 connector + cheap PSUs is a known fire-and-instability path. Pair with a Platinum-rated 1200 W+ PSU and high-quality cabling.
- Thermals in compact cases. 575 W of dissipation is a real cooling problem; small-form-factor builds throttle quickly without aggressive airflow.
- CUDA toolkit / driver lag for FP4. Engines are still catching up; expect a 6-12 month tail of "this engine doesn't yet use the 5090's FP4 path" through 2026.
- PCIe Gen5 x16 dependency. The 5090 wants Gen5 bandwidth for prefill on long contexts; older Gen4 boards still work but are bandwidth-limited.
- Multi-GPU absence of NVLink. Like the 4090, no NVLink — multi-card is PCIe only.
Alternatives by intent
| If you want… | Reach for |
|---|---|
| Cheaper, same-tier consumer | RTX 5080 (16 GB) or used RTX 4090 |
| Even more VRAM | RTX Pro 6000 Blackwell (96 GB) or RTX A6000 (48 GB) |
| 70B FP16 single-machine | Apple M3 Ultra 192 GB unified memory |
| AMD path | RX 9070 XT — much cheaper, ROCm tax applies |
| Datacenter throughput | H100 SXM or H200 |
Best pairings
- vLLM + 70B AWQ-INT4 — the canonical multi-user homelab default
- ExLlamaV2 + EXL2 4.65bpw + 70B — the single-stream king setup
- TensorRT-LLM FP4 path — the throughput-king path as engines mature through 2026
- Ubuntu 24.04 LTS + CUDA 12.6+ + Open WebUI in Docker — the homelab default
- Continue.dev routed at vLLM for the 32B-class coding agent — see /stacks/local-coding-agent
Who should avoid the RTX 5090
- Operators happy with 24 GB. A 4090 is dramatically better dollars-per-token; the 5090 only wins when 32 GB or FP4 matter.
- Anyone on a sub-1200 W PSU. Power delivery is non-negotiable.
- Compact-case builders without aggressive cooling. 575 W is a real thermal problem.
- Apple-ecosystem operators. Different stack entirely.
- Workloads where 13B-class models suffice. A 16 GB card saves ~$2000 at the same tier of usefulness.
Related
- Stacks: /stacks/local-coding-agent, /stacks/h100-tensor-parallel-workstation
- System guides: /guides/running-local-ai-on-multiple-gpus-2026, /systems/quantization-formats
- Tools: vLLM, TensorRT-LLM, ExLlamaV2
- Errors: /errors/wsl2-gpu-not-detected
Prices quoted in the review text are estimates from when it was written. Check the current price before you buy.
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Prefer it assembled?
If you would rather not build a PC around the NVIDIA GeForce RTX 5090 yourself, a configured desktop is the alternative.
Velocity Micro offers the NVIDIA GeForce RTX 5090 in its ProMagix HD80 (its configurator listed the card on 20 Oct 2026).
Velocity Micro builds configure-to-order desktops and workstations in the US. Compare the configured total, the power supply, cooling and warranty with buying the parts yourself before you order.
Configure a ProMagix HD80Affiliate link: RunLocalAI may earn a commission if you buy, at no extra cost to you. We have not tested a Velocity Micro system, and this is not a benchmark endorsement. How we make money.
Specs
| VRAM | 32 GB |
| Power draw (peak) | 575 W |
| Released | 2025 |
| MSRP | $1999 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 5090 with usable context.
Hardware worth comparing
The closest alternatives by price, memory bandwidth, and form factor, plus a step up and down — so you can frame the buying decision against real options.
Curated head-to-heads against specific cards — the buyer-decision shape that crosses VRAM bands.
The 5090 only justifies its price for buyers who specifically need 32 GB on one card or are running production image/video gen. The guides below cover those workloads.
Frequently asked
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Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.