| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 3090 | NVIDIA GeForce RTX 5070 Ti |
|---|---|---|---|
| Qwen 2.5 Coder 32B (Q4_K_M) | 23.10 GB | Fits Tight (+0.90 GB, ~35 tok/s) | Exceeds VRAM (-7.10 GB OOM) |
| Google Gemma 4 31B (Q4_K_M) | 21.44 GB | Fits Clean (+2.56 GB, ~37 tok/s) | Exceeds VRAM (-5.44 GB OOM) |
| Alibaba Wan 2.1 14B 720p (Q4 / NF4) | 18.15 GB | Runs Resident (+5.85 GB) | Overflow (-2.15 GB Block-Swap) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.43 GB | Comfort Margin (+9.57 GB) | Tight Fit (+1.57 GB, FP4 Boost) |
| FLUX.1 [dev] NF4 / 4-bit | 15.46 GB | Comfort Margin (+8.54 GB) | Tight Fit (+0.54 GB, Fast NVFP4) |
Comparing Ampere GA102 against Blackwell GB203 highlights why capacity beats generational shader improvements once models cross 16 GB. The RTX 3090 pairs twenty-four 1GB Micron GDDR6X packages in a double-sided clamshell layout across a wide 384-bit controller (936 GB/s), holding Qwen 2.5 Coder 32B (23.10 GB at Q4_K_M) and Google Gemma 4 31B (21.44 GB) natively without PCIe spill. By contrast, GB203 inside the RTX 5070 Ti relies on eight 2GB PAM3 28 Gbps GDDR7 chips over a 256-bit bus (896 GB/s) and adds 5th-gen Tensor Cores with hardware NVFP4 microscaling plus dual 9th-gen NVENC blocks. That silicon edge makes Blackwell dramatically faster during long-prompt prefill and 4-bit FP4 diffusion on 12B–20B models, yet the strict 16GB ceiling forces heavy CPU layer offload on every dense 32B checkpoint.
Before paying $799 for a Blackwell card or fighting thermal throttling on a used RTX 3090, run `llama-server -m model.gguf -ngl 99 -fa on -ctk q8_0 -ctv q8_0` and enforce `nvidia-smi -pl 285`. You keep 97% of 936 GB/s decode throughput while trimming junction heat and unlocking 32k context inside 24GB.
Matched Upgrade: Secondhand Ampere GA102 cards commonly suffer dried factory paste and hot rear GDDR6X memory; applying Thermal Grizzly PhaseSheet PTM cures 105°C hotspots under sustained 32B inference.
PhaseSheet PTM Pad on Amazon Prime ↗Zero monthly subscriptions, 100% private inference, and unmetered tokens for your homelab or workstation.
Skip the upfront hardware expense. Spin up secure cloud instances from $0.35/hr on Vast.ai or $0.79/hr on RunPod.
Only aggressively compressed IQ3_M or IQ3_XS weights (~14.8 GB total) squeeze inside 16GB with a short 4k context window, introducing noticeable syntax bugs on multi-file repository edits. By contrast, a 24GB RTX 3090 runs Qwen 2.5 Coder 32B Q4_K_M (23.10 GB) natively.
Because twelve GDDR6X modules sit on the rear PCB backplate behind aging factory pads, unserviced cards routinely exceed 104°C during continuous vLLM serving. Repasting the GA102 core with phase-change PTM and fresh 1.5mm rear pads cuts peak temperatures by 10–15°C.
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