Model × hardware check
Can Flint 40B run on NVIDIA GeForce RTX 5090 · 32 GB?
Yes, this is a comfortable estimate at 8,192 tokens. a coding-oriented MoE model that stores roughly 40B parameters but activates about 2.5B per token The RTX 5090's 32 GB frame buffer is the largest consumer tier in this catalog, with 1792 GB/s of memory bandwidth.
Short answer
Comfortable fit
At least 20% memory headroom remains after weights, KV cache, and runtime overhead.
- Available VRAM
- 32 GB
- Estimated requirement
- 20.2 GB
- Headroom
- +11.8 GB
- Fit ratio
- 158%
- Recommended quantization
- UD-Q3_K_XL
- Context ceiling
- 262,144 tokens
- Speed estimate
- Estimate · Very fast relative band
What the numbers say
The selected Flint 40B package uses 18.5 GB of weight files. The 8K KV cache estimate is 0.2 GB, and the formula adds 1.5 GB of runtime overhead. That produces a 20.2 GB requirement against 32 GB of VRAM.
The first 32 GB consumer-class target in this catalog. The extra capacity is most useful for larger quantized models or longer contexts.
The context ceiling shown above is the point where the same formula reaches the machine's capacity. It is not a promise that the backend can allocate that full context after the operating system and other applications take their share.
Quantization trade-offs at 8K
| Quantization | Weight files | 8K requirement | Fit on this machine | Context ceiling | Speed estimate | Ollama |
|---|---|---|---|---|---|---|
| UD-IQ1_M | 11.35 GB | 13.1 GB | Comfortable fit · 245% | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| UD-IQ2_XXS | 11.86 GB | 13.6 GB | Comfortable fit · 235% | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| UD-Q2_K_XL | 13.56 GB | 15.3 GB | Comfortable fit · 209% | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| UD-IQ3_XXS | 14.54 GB | 16.3 GB | Comfortable fit · 197% | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| UD-Q3_K_XL | 18.49 GB | 20.2 GB | Comfortable fit · 158% | 262,144 tokens | Estimate · Very fast relative band | No verified tag |
| NVFP4 | 22.86 GB | 24.6 GB | Comfortable fit · 130% | 262,144 tokens | Estimate · Very fast relative band | ollama run flint:40b |
| UD-Q4_K_XL | 25.05 GB | 26.8 GB | Tight fit · 119% | 189,902 tokens | Estimate · Very fast relative band | No verified tag |
| UD-Q5_K_XL | 29.92 GB | 31.7 GB | Tight fit · 101% | 20,244 tokens | Estimate · Very fast relative band | No verified tag |
| UD-Q6_K_XL | 36.32 GB | 38.1 GB | Won't fit · 84% | 0 tokens | Estimate · Very fast relative band | No verified tag |
| Q8_0 | 42.30 GB | 44.0 GB | Won't fit · 73% | 0 tokens | Estimate · Very fast relative band | No verified tag |
For MoE models, a tight CPU-offload result means the weights fit in system memory even when they do not fit in accelerator memory. That does not increase the accelerator context ceiling.
Run commands
lms get "Flint 40B"@ud-q3_k_xl
lms load <model-key-from-lms-ls> --gpu max --context-length 8192llama-cli -hf unsloth/flint-GGUF --hf-file flint-UD-Q3_K_XL.gguf -c 8192 -ngl 99 -p "Your prompt"Commands are examples, not a guarantee that the selected quantization tag is available in every runtime. Confirm the package name and license on the linked model page.
If it does not fit
This pairing already clears the comfort threshold
The current estimate leaves +11.8 GB. A longer context or another GPU application can reduce that margin, so verify free memory before relying on it.
Low-cost setup upgrades
Useful upgrades around NVIDIA GeForce RTX 5090 · 32 GB
These are optional setup improvements, not a claim that an accessory can increase GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate I earn from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Where this estimate can break
- Recursive's BF16 serving kit targets one B300 or H200 per replica. The lower-bit GGUF and Ollama builds trade quality and support for consumer hardware.
- The native context is 262K tokens; a long prompt can exhaust memory even after the weight file fits.
- Some community conversions and runtimes may lag the custom attention configuration. Verify the chosen runtime against the linked GGUF card.
- Other GPU applications, display buffers, and the operating system reduce usable memory.
- Longer prompts, concurrent requests, and a different backend can raise peak memory.
Compare this pairing
FAQ
Questions about this pairing.
How much memory does Flint 40B need on NVIDIA GeForce RTX 5090 · 32 GB?
At an 8K context with UD-Q3_K_XL, the estimate is 20.2 GB: 18.5 GB of weights, 0.2 GB of KV cache, and 1.5 GB of runtime overhead. The available VRAM is 32 GB.
What context length can NVIDIA GeForce RTX 5090 · 32 GB handle with this model?
The calculated ceiling is 262,144 tokens for this quantization, capped by the model's native 262,144-token context. Actual usable context can be lower after the OS, other applications, and backend workspace.
How does quantization change the memory requirement on NVIDIA GeForce RTX 5090 · 32 GB?
At 8,192 tokens, the published variants calculate as follows: UD-IQ1_M: 13.1 GB, comfortable fit; UD-IQ2_XXS: 13.6 GB, comfortable fit; UD-Q2_K_XL: 15.3 GB, comfortable fit; UD-IQ3_XXS: 16.3 GB, comfortable fit; UD-Q3_K_XL: 20.2 GB, comfortable fit; NVFP4: 24.6 GB, comfortable fit; UD-Q4_K_XL: 26.8 GB, tight fit; UD-Q5_K_XL: 31.7 GB, tight fit; UD-Q6_K_XL: 38.1 GB, won't fit; Q8_0: 44.0 GB, won't fit. A lower-bit file can move a result from too large to tight, but quality and kernel support may differ.
What can I run if Flint 40B does not fit on NVIDIA GeForce RTX 5090 · 32 GB?
This selection already clears the comfort threshold. If you later increase the context, monitor the ceiling of 262,144 tokens; smaller alternatives include NVIDIA Nemotron 3.5 Bolt E4B, LFM3-1.7B, Bonsai 2.
Is the speed estimate for Flint 40B on NVIDIA GeForce RTX 5090 · 32 GB measured?
No. Very fast relative band is a heuristic band derived from 2.5B active parameters and 1792 GB/s published memory bandwidth. Backend kernels, batch size, thermals, and CPU offload can change actual tokens per second.