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llama.cpp · homogeneous 2–4 GPUs · not a PC builder

Can this model run across multiple GPUs?

Phase 8 answers memory feasibility for identical cards under llama.cpp. Default split is layer (pipeline parallel: each GPU owns a slice of layers; KV stays with those layers). This is not 2 × VRAM = pool, and it is not a complete PC build.

2× AMD Radeon RX 570 8 GB

Does not fit on this configuration

Model
Qwen3 235B-A22B · Q4 · 8K
Runtime / split
llama.cpp · layer · calculator 1.0.0
Single-GPU status
DOES_NOT_FIT · required 151.2 GB vs usable 7.2 GB
Per card advertised / usable
8 GB / 7.2 GB
Aggregate advertised
16.0 GB — capacity, not effective model capacity
Nominal aggregate usable
14.4 GB (N × advertised × 0.90)
Effective per-GPU peak
76.0 GB vs usable 7.2 GB
Per-GPU split (approx.)
weights 69.0 GB · KV 0.7 GB · runtime 3.9 GB · safety 2.3 GB
Why
Even a 2K context exceeds per-GPU usable VRAM after partition and per-device reserves.
Topology
Requires space for 2 discrete GPUs. Exact cooler slot width is not in the RigForAI graph. layer split is pipeline parallel and can run over PCIe. KV stays with the layers on each GPU. NVLink is not treated as a 1× aggregate VRAM pool.
Performance
Phase 5 predictions are single-GPU. Multi-GPU tok/s is not published.
GPU-only current cost
Cost unavailable — no fresh matched Amazon offer for this canonical GPU.
Amazon
Formula
per_gpu_peak = ceil(weight_bytes/N) + ceil(kv_bytes/N) + runtime_base + safety_base + weight_fractions×ceil(weight_bytes/N). Compare to usable = advertised×0.90. Not (single_gpu_required × N) and not advertised_vram × N as effective capacity.

AMD Radeon RX 570 8 GB · Single-GPU can-run · Find a single GPU · Build a machine for this GPU setup

Query combinations are not indexed. Source: llama.cpp multi-GPU documentation.