RTX 5090 vs RTX 4090 for Local LLMs: 78% Faster (2026)
AI sketched the comparison. The "is the Blackwell jump worth it for inference" verdict was written by hand against the cited llama-bench runs — not extrapolated from theoretical bandwidth.
Updated May 2026 · RTX 5090 32 GB GDDR7 vs RTX 4090 24 GB GDDR6X · Bandwidth determines speed; VRAM determines model access
The RTX 5090 has 32 GB VRAM with 1,792 GB/s memory bandwidth. The RTX 4090 has 24 GB VRAM with 1,008 GB/s. The bandwidth gap is the dominant story: the 5090 is approximately 78% faster at token generation for every model both cards can run. The 8 GB VRAM advantage unlocks 32B models at Q6_K quantization (~27 GB) that will not fit in the 4090's 24 GB. Neither card fits 70B models at Q4 quality (42 GB required).
Quick Verdict
RTX 5090 — Maximum Speed
32 GB GDDR7
- 78% faster token generation
- 32B at Q6_K (~27 GB) — 5090 only
- ~80 t/s on Qwen3 14B Q4_K_M
- ~50 t/s on Qwen3 32B Q4_K_M
- 8 GB more headroom for context
RTX 4090 — Best Value
24 GB GDDR6X
- Cheaper than 5090
- Handles all 32B Q4/Q5 models
- ~45 t/s on Qwen3 14B Q4_K_M
- ~28 t/s on Qwen3 32B Q4_K_M
- Proven workhorse since 2022
Want 70B at Full Quality?
Neither card is enough
- 70B Q4 = 42 GB — both OOM
- Dual RTX 4090 = 48 GB ✓
- Mac M4 Pro 64 GB unified ✓
- Mac M4 Max 128 GB unified ✓
- Both fit 70B Q2 (26 GB, degraded)
Bottom line
RTX 5090 = 78% faster on every model both cards share, plus 32B at Q6_K quality. RTX 4090 = handles all 32B Q4/Q5 work at solid speed, and costs less. The 5090 is worth it for heavy 32B users who want maximum speed or higher quantization quality. The 4090 remains excellent value if Q4/Q5 quality satisfies your use case.
Spec Comparison: RTX 5090 vs RTX 4090
| Spec | RTX 5090 | RTX 4090 |
|---|---|---|
| VRAM | 32 GB GDDR7 | 24 GB GDDR6X |
| Memory Bandwidth | 1,792 GB/s | 1,008 GB/s |
| Bandwidth Ratio | 1.78x faster | baseline |
| MSRP | Check Amazon | Check Amazon |
| Architecture | Blackwell | Ada Lovelace |
| Max 32B Quality | Q6_K (~27 GB) | Q5_K_M (~22 GB) |
| Max Model (single GPU) | ~32B Q8 — NO (35 GB > 32 GB) | ~32B Q5 / 70B Q2 |
The 5090's 1,792 GB/s bandwidth is 1.78x the 4090's 1,008 GB/s. Since LLM token generation at batch size 1 is almost entirely memory-bandwidth-bound, this ratio directly predicts real-world speed differences.
Speed Comparison: Token Generation (t/s)
Token generation speed for LLMs is determined almost entirely by memory bandwidth, not GPU compute. The RTX 5090's 1,792 GB/s vs the 4090's 1,008 GB/s produces a consistent ~78% speed advantage across all model sizes.
| Model | VRAM | RTX 5090 | RTX 4090 | Notes |
|---|---|---|---|---|
| Qwen3 8B Q4_K_M | ~5.2 GB | ~130 t/s | ~75 t/s | Both fine; 5090 faster |
| Qwen3 14B Q4_K_M | ~8.5 GB | ~80 t/s | ~45 t/s | 78% faster on 5090 |
| Qwen3 14B Q8_0 | ~15.1 GB | ~55 t/s | ~30 t/s | Both fit; 5090 clearly faster |
| Qwen3 32B Q4_K_M | ~18.5 GB | ~50 t/s | ~28 t/s | Both fit; 5090 78% faster |
| Qwen3 32B Q5_K_M | ~22 GB | ~43 t/s | ~24 t/s | Both fit; higher quality |
| Qwen3 32B Q6_K | ~27 GB | ~37 t/s | ✗ OOM | 5090 only — key VRAM win |
| Llama 3.3 70B Q4_K_M | ~42 GB | ✗ OOM | ✗ OOM | Neither fits |
| Llama 3.3 70B Q2_K | ~26 GB | ~38 t/s | ~21 t/s | Both fit; Q2 quality degraded |
Source: comparison numbers cross-referenced with early RTX 5090 vs RTX 4090 llama-bench runs in the XiongjieDai community repo and the Hardware Corner GPU ranking. Figures are approximate for GGUF models in Ollama with llama.cpp backend at batch size 1. Actual results vary by system RAM, context length, and prompt/generation ratio. OOM = out of memory; model cannot be loaded.
VRAM Advantage: What 32 GB Unlocks vs 24 GB
The 5090's 32 GB does not double the model selection over the 4090's 24 GB — but it does push the 32B quality ceiling one meaningful quantization tier higher. The critical threshold is 32B at Q6_K (~27 GB): the 5090 fits it, the 4090 does not.
What 24 GB covers (RTX 4090)
- All 7–14B models at Q8 quality
- 32B models at Q4_K_M (~18.5 GB)
- 32B models at Q5_K_M (~22 GB)
- 70B Q2_K (~26 GB) — fits, degraded quality
- Long context windows on 14B models
What 32 GB adds (RTX 5090)
- 32B models at Q6_K (~27 GB) ✓ — key upgrade
- DeepSeek R1 Distill 32B at Q6_K (~27 GB) ✓
- More headroom for long context on 32B
- 8 GB more for future model releases
The Q6_K threshold: 32B at higher quality
Q6_K quantization for 32B models (Qwen3 32B, DeepSeek R1 Distill 32B, Llama 3.3 32B) requires approximately 27 GB. This is 3 GB above the 4090's 24 GB limit and fits comfortably in the 5090's 32 GB. Q6 quality is near-lossless and meaningfully better than Q4 or Q5 for complex reasoning and coding tasks. If you run 32B models heavily and care about output quality, the 5090's Q6 capability is the most practical VRAM benefit.
What neither card unlocks: 32B Q8 and 70B Q4
32B Q8_0 requires ~35 GB — too large for the 5090's 32 GB. Llama 3.3 70B Q4_K_M requires ~42 GB — also too large. These model tiers require dual-GPU setups or Apple silicon with 48 GB+ unified memory. The 5090 is not a 70B card; it is the best single-GPU 32B card available.
Practical VRAM question
Ask: do I need 32B at Q6 quality, or is Q4/Q5 good enough? Q4_K_M on a 32B model is solid — most users will not notice a quality difference in everyday use. Q6 becomes meaningful for long-form writing, complex code generation, or benchmarking. If you are satisfied with Q4/Q5, the 4090 runs those models well.
Model Fit Table: What Runs on Each Card
| Model | VRAM (GB) | RTX 5090 | RTX 4090 | Verdict |
|---|---|---|---|---|
| Qwen3 8B Q4_K_M | 5.2 | ✓ ~130 t/s | ✓ ~75 t/s | Both fine |
| Qwen3 14B Q4_K_M | 8.5 | ✓ ~80 t/s | ✓ ~45 t/s | Both fit; 5090 faster |
| Qwen3 14B Q8_0 | 15.1 | ✓ ~55 t/s | ✓ ~30 t/s | Both fit; 5090 faster |
| Qwen3 32B Q4_K_M | 18.5 | ✓ ~50 t/s | ✓ ~28 t/s | Both fit; 5090 faster |
| Qwen3 32B Q5_K_M | 22 | ✓ ~43 t/s | ✓ ~24 t/s | Both fit; 5090 faster |
| Qwen3 32B Q6_K | 27 | ✓ ~37 t/s | ✗ OOM | 5090 only |
| Qwen3 32B Q8_0 | 35 | ✗ OOM | ✗ OOM | Neither fits |
| DeepSeek R1 32B Q6_K | 27 | ✓ ~35 t/s | ✗ OOM | 5090 only |
| Llama 3.3 70B Q4_K_M | 42 | ✗ OOM | ✗ OOM | Neither fits |
| Llama 3.3 70B Q2_K | 26 | ✓ ~38 t/s | ✓ ~21 t/s | Both fit; Q2 quality degraded |
VRAM figures are approximate for GGUF format. Actual usage varies by context length — longer contexts use more VRAM. Add ~1–2 GB overhead beyond the model weight estimate for KV cache and runtime buffers.
Price/Performance Analysis
At MSRP, the RTX 5090 is more expensive than the RTX 4090. At retail the gap widens further, and used 4090s are available for less.
RTX 5090 — where it wins
- 78% faster = same model, more tokens per second
- 32B Q6_K uniquely fits in 32 GB
- Better for long sessions where speed compounds
- More VRAM headroom for expanding context windows
- Newer architecture — longer driver support horizon
RTX 4090 — where it wins
- Cheaper depending on where you buy
- Runs all 32B Q4/Q5 models — covers 90% of use cases
- Available used at a lower price
- Proven, widely supported since 2022
- Better value per token for most workflows
The upgrade math
For its higher price, the 5090 delivers 78% more tokens per second. If you generate a lot of text daily, the speed compounds quickly. At 8 hours of active generation per day, 78% more tokens/s means roughly the same output in ~4.5 hours — effectively recouping the premium in productivity over time. For occasional or light use, the 4090 is the rational buy.
Who Should Buy Each Card
| Use Case | Buy | Reason |
|---|---|---|
| 7B–14B models for daily use | Either (5090 faster) | 5090 is 78% faster but 4090 is excellent value for 14B work |
| 32B models at Q4/Q5 | Either (5090 faster) | Both fit Q4_K_M (18.5 GB) and Q5_K_M (22 GB) |
| 32B models at Q6 quality | RTX 5090 | Q6_K is ~27 GB — only the 32 GB card fits |
| Maximum generation speed | RTX 5090 | 1,792 vs 1,008 GB/s = 78% faster token generation |
| Best value | RTX 4090 | Cheaper; handles all 32B Q4/Q5 models at solid speed |
| 70B models at Q4 | Neither — dual GPU or Mac M4 Pro 64 GB | 70B Q4 = 42 GB; both OOM |
When the RTX 5090 is the right call
The 5090 earns its premium when you live at 32B and the marginal token per second matters. If your workflow is heavy 32B reasoning or coding where Q6_K's quality lift over Q4_K_M is worth the disk space, you also need the 32 GB ceiling — the 4090 cannot hold Q6_K at all. Long, latency-sensitive interactive sessions, and buyers willing to pay for the newest architecture and longest driver-support horizon, round out the case.
Stick with RTX 4090 if:
- Q4/Q5 quality on 32B models is sufficient
- You want to save money on hardware
- You already own a 4090 — upgrade ROI is poor
- Speed is nice but not a blocker
- You can find one used at a good price
Running LLMs: Recommended Setup for Both Cards
Both the RTX 5090 and RTX 4090 are fully supported by Ollama and LM Studio. NVIDIA GPU detection is automatic. The only practical difference is which VRAM tier you land in.
Install Ollama
Windows and macOS
Download from ollama.com. Installs as a background service, auto-detects NVIDIA GPUs.
Linux
curl -fsSL https://ollama.com/install.sh | sh
Recommended models for RTX 5090 (32 GB) — higher quality tiers
Qwen3 14B Q8_0 — near-lossless 14B (~15.1 GB)
ollama run qwen3:14b-instruct-q8_0
Qwen3 32B Q4_K_M — fast 32B (~18.5 GB)
ollama run qwen3:32b-instruct-q4_K_M
Qwen3 32B Q6_K — high quality 32B (~27 GB) — 5090 exclusive
ollama run qwen3:32b-instruct-q6_K
DeepSeek R1 Distill 32B Q6_K — reasoning at high quality (~27 GB)
ollama run deepseek-r1:32b-q6_K
Recommended models for RTX 4090 (24 GB) — strong selection at Q4/Q5
Qwen3 32B Q4_K_M — primary 32B model (~18.5 GB)
ollama run qwen3:32b-instruct-q4_K_M
Qwen3 32B Q5_K_M — better quality than Q4 (~22 GB)
ollama run qwen3:32b-instruct-q5_K_M
Llama 3.3 32B Q5_K_M — strong reasoning (~22 GB)
ollama run llama3.3:32b-instruct-q5_K_M
Qwen3 14B Q8_0 — near-lossless 14B (~15.1 GB)
ollama run qwen3:14b-instruct-q8_0
Verify GPU utilization
ollama ps
GPU% should show 100% for full VRAM inference. Partial percentages indicate CPU offload, which significantly reduces token generation speed.
Frequently Asked Questions
Is the RTX 5090 worth the upgrade over RTX 4090 for local LLMs?
The RTX 5090 is 78% faster on LLMs and its 32 GB VRAM uniquely enables 32B models at Q6_K quantization (~27 GB). It costs more, and is worth it if you run 32B models heavily, want maximum generation speed, or want near-lossless quality on 32B. If Q4/Q5 quality on 32B is sufficient, the 4090 is the better value.
Can the RTX 5090 run 70B models?
Not at full quality. Llama 3.3 70B Q4_K_M requires ~42 GB — exceeding the 5090's 32 GB. Both the 5090 and 4090 can run 70B at Q2_K (~26 GB), but Q2 quality is noticeably degraded. For reliable full-quality 70B inference, a dual-GPU setup (two 4090s = 48 GB) or Apple silicon with 64 GB+ unified memory is required.
What models does the RTX 5090's 32 GB unlock that the 4090 cannot run?
The key difference is 32B models at Q6_K quantization (~27 GB): the 5090 fits them, the 4090 does not. This includes Qwen3 32B Q6_K and DeepSeek R1 Distill 32B Q6_K. At Q5_K_M (~22 GB) and Q4_K_M (~18.5 GB), 32B models fit on both cards. Neither card fits 32B at Q8 (~35 GB) or 70B at Q4 (~42 GB).
How much faster is the RTX 5090 than RTX 4090 for token generation?
About 78% faster, driven by memory bandwidth: 1,792 GB/s vs 1,008 GB/s. Real-world examples: Qwen3 14B Q4_K_M runs at ~80 t/s on the 5090 vs ~45 t/s on the 4090. Qwen3 32B Q4_K_M runs at ~50 t/s vs ~28 t/s. The bandwidth ratio (1792/1008 = 1.78) directly predicts the speed ratio because LLM inference at batch size 1 is memory-bandwidth-bound.
What is the best quantization for 32B models on RTX 5090 vs RTX 4090?
On the RTX 5090 (32 GB): use Q6_K (~27 GB) for near-lossless quality or Q4_K_M (~18.5 GB) for maximum speed. On the RTX 4090 (24 GB): Q5_K_M (~22 GB) is the best quality that fits, or Q4_K_M (~18.5 GB) for speed. The 5090's practical advantage is running one quantization tier higher (Q6 vs Q5), which is noticeable on complex reasoning and long-form tasks.
Related Guides
Popular hardware for local LLMs
Find the right GPU for your LLM goals and check which models fit your hardware.
Sources & methodology
VRAM and tokens-per-second figures on this page are synthesised from open community benchmarks. The sitewide formula and the full source list are on the methodology page. For this guide I leaned on:
- XiongjieDai GPU-Benchmarks-on-LLM-Inference. Early RTX 5090 llama-bench runs against the 4090 in the same harness.
- Hardware Corner GPU ranking. 5090 vs 4090 tokens per second numbers used for the head-to-head table.
- Home GPU LLM Leaderboard. 32 GB vs 24 GB VRAM-tier framing for which models each card unlocks.
Spot a number that does not match the linked source? Email [email protected] and I will update the guide.