The 24 GB build

One used RTX 3090 on the 48 GB build's AM5 platform, for 30B-class models at Q4 today and a second card later.

Verdict

For a builder who wants 30B-class models on one card now and a machine that becomes the 48 GB build by adding a second used RTX 3090 later. 24 GB holds Qwen2.5 32B at Q4_K_M with an 8k context, or the Qwen3 30B-A3B mixture of experts at Q4_K_M with 32k. One used 3090 is the cheapest path to 24 GB on one card; the rest of the parts list is the 48 GB platform unchanged, so the board, PSU and case cost more than a single card needs. The card is rated at 350 W; power-limited to 300 W, this site's estimate puts the machine at about 611 W at the wall while generating.

Model fit

Model Quantisation Context Note
Qwen2.5 32B Instruct Q4_K_M GGUF (19.85 GB file) 8k File plus about 2.1 GB of 16-bit KV cache comes to about 22.0 GB against 24 GB (25.77 GB); arithmetic in the FAQ. 16k leaves about 1.6 GB, borderline. Estimated, not measured.
Qwen3 30B-A3B (MoE, 3B active) Q4_K_M GGUF (18.63 GB file) 32k File plus about 3.2 GB of 16-bit KV cache comes to about 21.9 GB. Its config allows 40,960 tokens, which comes to about 22.7 GB. Estimated, not measured.
Qwen2.5 14B Instruct Q8_0 GGUF (15.70 GB file) 32k (the model's native maximum) File plus about 6.4 GB of 16-bit KV cache comes to about 22.1 GB: the Starter Tier's 14B model at a higher-quality quantisation and its full context. Estimated, not measured.

Parts list

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Qty Part Buy USBuy DEBuy UK
1 NVIDIA GeForce RTX 3090 (Founders Edition)
VRAM
24 GB
Memory bandwidth
936 GB/s
TDP
350 W
Slot width
3 slots
Length
313 mm
Power connectors
1x 12-pin
Cooler
flow-through
VRAM/W
0.069 GB/W
VRAM/slot
8.0 GB
1 Alternate NVIDIA GeForce RTX 4090 (Founders Edition)
VRAM
24 GB
Memory bandwidth
1,008 GB/s
TDP
450 W
Slot width
3 slots
Length
304 mm
Cooler
flow-through
VRAM/W
0.053 GB/W
VRAM/slot
8.0 GB
1 ASRock X870E Taichi
Socket
AM5
Platform
AM5
Max GPUs
2
Lanes/GPU
8
x16 slots
2
Max RAM
256 GB
Form factor
E-ATX
Needs risers
No
1 AMD Ryzen 9 9950X
Socket
AM5
Cores
16
PCIe lanes
24
PCIe gen
5
Memory channels
2
TDP
170 W
1 Kingston FURY Beast 128GB (2x64GB) DDR5-5600 (KF556C36BBEK2-128)
Capacity per kit
128 GB
Modules
2
Type
DDR5 UDIMM
Speed
5,600 MT/s
ECC
No
not available
1 Samsung 990 PRO 2TB
Capacity
2,000 GB
PCIe gen
4
Seq. read
7,450 MB/s
Endurance
1,200 TBW
1 Alternate WD_BLACK SN850X 4TB
Capacity
4,000 GB
PCIe gen
4
Seq. read
7,300 MB/s
Endurance
2,400 TBW
1 Corsair HX1200i (2025, ATX 3.1)
Wattage
1,200 W
Efficiency
80+ Platinum
8-pin PCIe
5
12V-2x6
2
ATX 3.x
Yes
Length
200 mm
GPUs supported
2
1 Fractal Design Define 7 XL (Black Solid)
Type
case
GPUs supported
4
Max GPU length
549 mm
Riser mounts
Yes
Max board
SSI-EEB
1 Alternate Phanteks Enthoo Pro 2 Server Edition (tempered glass)
Type
case
GPUs supported
4
Max GPU length
503 mm
Riser mounts
Yes
Max board
SSI-EEB
1 Noctua NH-D15 G2
Socket
AM5
Type
tower-air
Height
168 mm
Fan size
140 mm
Max noise
24.8 dB(A)

Basis

GPUs GPU model Board Source
1 RTX 3090 Unknown skeptric.com
1 RTX 3090 ASUS ROG Crosshair VIII Hero WiFi blog-5kl.pages.dev
1 RTX 3090 ASUS ROG STRIX Z790-F biagioantonelli.github.io

Compatibility warnings

Power & noise

Estimated 24/7 energy use, with every assumption stated:

Arithmetic: 4 h × 0.611 kW ≈ 2.4 kWh, plus 20 h × 0.096 kW ≈ 1.9 kWh, gives about 4.4 kWh a day at the wall. That is about 132 kWh a month (× 30.4 days) and about 1,590 kWh a year (× 365). Nearly half of it is idle draw, which a platform sized for two cards carries even with one; the duty cycle moves the result far more than any part choice here. Multiply by your own tariff. The power and noise guide explains the method and its weakest inputs.

Noise: the 3090 is the loud part, and none of the Basis builds measured noise. With one card, nothing below it blows hot air into it, which hav4ik names as the reason closely stacked 30-series cards overheat; skeptric’s 3090 stayed under about 75 °C in a benchmark and did not pass 71 °C during training, in a Corsair 4000D Airflow with four extra 120 mm fans. Power-limiting to 300 W lowers both heat and fan speed. The Define 7 XL is sound-damped, but the damping also holds heat, so fill the front fan mounts. timroy keeps his machine in the basement rather than listen to it in his office, which is the other answer.

FAQ

Has anyone built exactly this list? Not that we know of. The three Basis builds share its core: one used RTX 3090 in a desktop, run for machine learning. Their platforms differ. skeptric’s is a Ryzen 5 5600G on a B550 board with an 850 W PSU in a Corsair 4000D Airflow. timroy’s is a Ryzen 9 5900X on an ASUS Crosshair VIII Hero with a 1200 W PSU in the original Phanteks Enthoo Pro. biagioantonelli’s is a Core i7-13700K on an ASUS Strix Z790-F with a 1300 W PSU in a Lian Li O11 Dynamic. None reports a tokens-per-second figure for a language model, so every Model fit row is our arithmetic.

Why build for two cards when I’m buying one? timroy, one of the Basis builders, planned for it: he chose his 1200 W PSU partly for a second GPU and expects to need a second 24 GB card for fine-tuning; his board drops its first slot to x8 with two cards installed, and the second slot is only half wired. This Tier’s Taichi runs both slots at x8 from the CPU instead. Buying the two-card PSU and board now means the upgrade is a second 3090 and nothing else.

How was the fit worked out? The VRAM sizing guide explains the method. The card holds 24,576 MiB, about 25.77 GB. Qwen2.5 32B has 64 layers, 8 key/value heads and a head size of 128 (5,120 ÷ 40 heads). A 16-bit KV cache, llama.cpp’s default, therefore takes 64 × 8 × 128 × 2 (keys and values) × 2 bytes = 262,144 bytes per token. At 8,192 tokens that is about 2.15 GB; with the 19.85 GB Q4_K_M file, about 22.0 GB, leaving roughly 3.8 GB for the CUDA context and compute buffers. At 16k the cache is 4.29 GB and the margin falls to about 1.6 GB. Qwen3 30B-A3B (48 layers, 4 key/value heads, head size 128) takes 98,304 bytes per token, about 3.22 GB at 32k. Qwen2.5 14B (48 layers, 8 key/value heads, head size 128) takes 196,608 bytes per token, about 6.44 GB at 32k.

Why the 3090 and not the 4090 alternate? Both have 24 GB, so they fit the same models. The 4090 has more memory bandwidth (1,008 GB/s against 936 GB/s) and much more compute, so it processes prompts faster. It costs much more than a used 3090, draws 450 W against 350 W, and the 48 GB build is two 3090s, so the 3090 is the card that carries over.

What’s the upgrade path? The 48 GB build: add a second used RTX 3090 in PCIE2, set the same 300 W limit on both, and you have that Tier’s main parts list. 48 GB fits Llama 3.3 70B at Q4_K_M with an 8k context, which 24 GB cannot hold: its 42.52 GB file alone is well over one card.

Sources
  1. https://www.skeptric.com/deep-learning-build-2k/
  2. https://blog-5kl.pages.dev/machine-learning/llm-rig
  3. https://biagioantonelli.github.io/blog/2024/01/29/DLrig
  4. https://huggingface.co/bartowski/Qwen2.5-32B-Instruct-GGUF
  5. https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/config.json
  6. https://huggingface.co/bartowski/Qwen_Qwen3-30B-A3B-GGUF
  7. https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/config.json
  8. https://huggingface.co/bartowski/Qwen2.5-14B-Instruct-GGUF
  9. https://huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF
  10. https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/config.json
  11. https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
  12. https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
  13. https://www.igorslab.de/en/nvidia-geforce-rtx-3090-founders-edition-review-between-even-between-evaluate-and-common-decadence-if-price-is-not-all/16/
  14. https://hav4ik.github.io/meet-iva-my-dl-rig
  15. https://download.asrock.com/Manual/X870E%20Taichi.pdf#page=6
  16. https://download.asrock.com/Manual/X870E%20Taichi.pdf#page=7
  17. https://download.asrock.com/Manual/X870E%20Taichi.pdf#page=11
  18. https://assets.fractal-design.com/files/uxzbxy2o/production/5132765576dd5fff6c58e2e7fe06d7ff98f78ef7.pdf#page=6
  19. https://phanteks.com/product/enthoo-pro-2-server-edition-tg/
  20. https://www.corsair.com/us/en/p/psu/cp-9020307-na/hx1200i-fully-modular-ultra-low-noise-atx-power-supply-cp-9020307-na
  21. https://www.corsair.com/us/en/p/psu/cp-9020309-na/hx1500i-fully-modular-ultra-low-noise-atx-power-supply-cp-9020309-na
  22. https://www.amd.com/en/products/processors/desktops/ryzen/9000-series/amd-ryzen-9-9950x.html
  23. https://www.noctua.at/en/compatibility/by-components/motherboards/asrock-x870e-taichi
  24. https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md
  25. https://en.wikipedia.org/wiki/80_Plus
  26. https://michaelbluejay.com/electricity/maxload.html
  27. https://plugsocketmuseum.nl/RadialRingCircuit.html
  28. https://plugsocketmuseum.nl/Schuko1.html
  29. https://docs.nvidia.com/deploy/nvidia-smi/index.html
  30. https://www.pugetsystems.com/labs/hpc/Quad-RTX3090-GPU-Power-Limiting-with-Systemd-and-Nvidia-smi-1983/

Last reviewed

  • — First published.
  • — Linked the sizing and operating guides.