NVIDIA RTX 5090 vs NVIDIA RTX 4090
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.
Quick verdict
NVIDIA RTX 5090 wins for local AI inference. It has 8 GB more VRAM and 78% more memory bandwidth, runs 47 models natively (vs 42), and exclusively fits 5 models the other cannot.
Analysis
The NVIDIA RTX 5090 succeeds the RTX 4090 as NVIDIA's flagship consumer GPU, moving from Ada Lovelace to Blackwell architecture. For local AI inference, this is not a routine generational refresh — the 5090 brings a 78% memory bandwidth increase and 33% more VRAM, two numbers that directly determine which models you can run and how fast they generate tokens. At roughly $2,000, the 5090 lands at the same price tier the 4090 occupied at launch, making this a pure generation-over-generation value question.
The RTX 5090's 32 GB of GDDR7 on a 512-bit bus delivers 1,792 GB/s — nearly double the 4090's 1,008 GB/s from its 24 GB of GDDR6X on a 384-bit bus. In practice, that 78% bandwidth gap means the 5090 generates tokens roughly 1.8× faster on any model both cards can hold in VRAM. The extra 8 GB is equally consequential: the 4090 tops out at running 30B-class models like Qwen 3-32B at Q4_K_M, with 70B models requiring CPU offload and its steep speed penalty. The 5090 handles those same 30B models at Q8_0 with room to spare and can fit some 70B models at Q4_K_M natively — a meaningful threshold, since 70B-class models (Llama 3.3 70B, Qwen 2.5-72B, DeepSeek-R1-Distill-Llama-70B) represent the current quality ceiling for open-weight local inference. Blackwell's 5th-gen Tensor Cores also add FP4 support, which inference engines like TensorRT-LLM and llama.cpp are increasingly targeting for higher throughput at equivalent quality — a capability Ada Lovelace simply lacks.
Bottom line: The RTX 5090 is the clear winner for local AI inference in absolute terms: more VRAM, dramatically more bandwidth, and a newer architecture with FP4 support. If you are building a new AI workstation in 2025, the 5090 is the obvious choice at roughly $2,000. The RTX 4090 remains defensible only if you find one at a significant discount on the used market and your workload stays comfortably within 24 GB — 7B–14B models at high quantization, or 30B models at Q4. For anyone running or planning to run 30B–70B class models locally, the 5090's extra VRAM and bandwidth justify the price difference on their own. The generational leap from Ada to Blackwell is one of the largest in NVIDIA's consumer GPU history for AI inference workloads.
Specs comparison
| Spec | NVIDIA RTX 5090 | NVIDIA RTX 4090 |
|---|---|---|
| VRAM | 32 GB | 24 GB |
| Memory type | GDDR7 | GDDR6X |
| Bandwidth | 1792 GB/s(+78%) | 1008 GB/s |
| Architecture | Blackwell | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Consumer | Consumer |
| Released | 2025 | 2022 |
| Models (native) | 47 | 42 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 5090 | NVIDIA RTX 4090 | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 77.8 t/s(Q2_K) | — | — |
| Qwen 3.6 27B(27B) | 132.7 t/s(NVFP4) | 74.7 t/s(NVFP4) | +78% |
| Llama 3.1 8B Instruct(8B) | 112 t/s(BF16) | 63 t/s(BF16) | +78% |
| Qwen 2.5 7B Instruct(7.6B) | 117.9 t/s(BF16) | 66.3 t/s(BF16) | +78% |
Delta is NVIDIA RTX 5090 relative to NVIDIA RTX 4090.
Only NVIDIA RTX 5090 can run(5)
Only NVIDIA RTX 4090 can run(0)
No exclusive models — NVIDIA RTX 5090 can run everything NVIDIA RTX 4090 can.
Both run natively(42)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x7B Instruct v0.1305.6 t/svs261.3 t/s
- Qwen 3.5 35B-A3B (MoE)1314.1 t/svs739.2 t/s
- Qwen 3.6 35B102.4 t/svs57.6 t/s
- Yi 1.5 34B Chat104.2 t/svs58.6 t/s
- Qwen3 32B109.3 t/svs61.5 t/s
- Qwen 2.5 32B Instruct110.3 t/svs62 t/s
- Qwen 2.5 Coder 32B Instruct110.3 t/svs62 t/s
- DeepSeek R1 Distill Qwen 32B110.3 t/svs62 t/s
- Nemotron 3 Nano 30B1314.1 t/svs739.2 t/s
- Gemma 4 31B115.6 t/svs65 t/s
- Qwen3 30B-A3B (MoE)1314.1 t/svs739.2 t/s
- Gemma 2 27B Instruct131.8 t/svs74.1 t/s
- Gemma 3 27B Instruct132.7 t/svs74.7 t/s
- Qwen 3.6 27B132.7 t/svs74.7 t/s
- Gemma 4 26B (MoE)1037.5 t/svs583.6 t/s
- Mistral Small 3.1 24B Instruct149.3 t/svs84 t/s
- +26 more on both
Which should you choose?
- • You need to run larger models (>24 GB VRAM)
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX 5090 or NVIDIA RTX 4090?
- For local AI inference, the NVIDIA RTX 5090 has the edge. It offers 32 GB VRAM (vs 24 GB) and 1792 GB/s bandwidth (vs 1008 GB/s), letting it run 47 models natively in VRAM vs 42 for its rival.
- How much VRAM does the NVIDIA RTX 5090 have vs the NVIDIA RTX 4090?
- The NVIDIA RTX 5090 has 32 GB of GDDR7 at 1792 GB/s. The NVIDIA RTX 4090 has 24 GB of GDDR6X at 1008 GB/s. The NVIDIA RTX 5090 has 8 GB more VRAM, allowing it to run 5 models the NVIDIA RTX 4090 cannot fit natively.
- Can the NVIDIA RTX 5090 run Llama 3.3 70B?
- Yes. The NVIDIA RTX 5090 runs Llama 3.3 70B natively at Q2_K quantization at approximately 77.8 tokens per second.
- Can the NVIDIA RTX 4090 run Llama 3.3 70B?
- The NVIDIA RTX 4090 can run Llama 3.3 70B with CPU offload at NVFP4, but at reduced speed.
- What is the difference between the NVIDIA RTX 5090 and NVIDIA RTX 4090 for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 5090 has 32 GB VRAM at 1792 GB/s (CUDA backend). The NVIDIA RTX 4090 has 24 GB VRAM at 1008 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 5090 runs 47 models natively vs 42 for the NVIDIA RTX 4090.