We focus on models like Llama 3, DeepSeek, and Mistral to support the decentralized AI movement. We prioritize VRAM capacity over gaming benchmark scores.
Joined March 2026
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Muse Glimmer 30B VRAM floors (text-only):
Q4: ~17-20GB → single RTX 3090/4090
Q5/Q6: ~22-28GB → 32GB or dual 16GB
+ images: spikes past 24GB
30B agentic, Apache 2.0, one-card local.
VRAM map: aicomputerguide.com/tools/wi…
ALT Muse Glimmer 30B VRAM floors: Q4 17-20GB fits 24GB cards, Q5/Q6 needs 32GB+, image inputs over 24GB
1.8 tok/s vs 31.5 tok/s on DeepSeek-R1 70B.
Single 24GB GPU (RTX 3090/4090) forces 44% CPU offload on 70B Q4 = total bottleneck.
Dual 16GB GPUs (32GB total) hold full IQ4_XS quant for 17.5x faster generation.
Full hardware benchmark guide: aicomputerguide.com/guides/b…
$31.25/GB VRAM vs $75.00/GB VRAM.
1x RTX 4090 ($1,800) = 24GB VRAM (OOM on 70B Q4)
2x RTX 3090 ($1,500) = 48GB VRAM (38 tok/s on 70B Q4)
For local LLMs, VRAM capacity > single-card clock speed.
Dual GPU setup guide & benchmarks: aicomputerguide.com/guides
16GB VRAM capacity map:
14B Q5 → ~9-11GB (headroom)
32B Q4 → ~12-14GB (tight at 4k)
34B Q3/Q4 → 4k only
70B → needs 24GB+
Spill to system RAM = 10-40× slower.
Guide: aicomputerguide.com/builds/m…
ALT 16GB VRAM local LLM capacity map: 14B Q5, 32B Q4, 34B Q3/Q4, 70B no-go
“You need a 4090 for local LLMs.”
False.
8GB → 7-8B Q5
12-16GB → 14B Q5 / 32B Q4
24GB → 70B Q3/Q4
48GB+ → 70B Q5 or dual-GPU Q8
Match VRAM to the model, not the marketing SKU.
VRAM floors: aicomputerguide.com/
ALT Infographic myth-busting you need a 4090 for local LLMs: VRAM floors 8GB 7-8B Q5, 12-16GB 14B Q5 or 32B Q4, 24GB 70B Q3/Q4, 48GB+ 70B Q5 dual-GPU
61 tok/s vs 52 vs 44 on the same 14B Q5.
llama.cpp · Ollama · LM Studio on a 24GB RTX 3090 (4k ctx).
Convenience costs throughput. Pick the stack for the job, not the logo.
Runtime guide: aicomputerguide.com/guides/
ALT Infographic comparing llama.cpp 61 tok/s, Ollama 52 tok/s, and LM Studio 44 tok/s on Qwen 2.5 14B Q5_K_M on RTX 3090 24GB
$37.44/GB VRAM vs $49.93/GB VRAM.
AMD RX 9070 XT 16GB ($599) gives the same 16GB VRAM buffer for local LLMs as the RTX 4070 Ti Super ($799) at 25% lower $/GB.
ROCm 6.3 runs Ollama at ~44 tok/s on Qwen 2.5 14B Q8.
Full 16GB guide: aicomputerguide.com/guides/
DeepSeek R1 Distill 32B does NOT require a 32GB GPU.
At Q4_K_M quantization, 32B weights + 8k context fit in 20GB VRAM with ~99% benchmark retention.
A single 24GB RTX 3090 handles it easily.
Full VRAM breakdown & hardware guide: aicomputerguide.com
Dual RTX 3090 gives you 48GB VRAM at $31.25/GB and 38 tok/s on Llama 3 70B (Q4).
Compare that to a single RTX 4090 ($74.95/GB) or RTX 4070 Ti Super ($49.93/GB).
Full VRAM $/GB leaderboard & setup guide: aicomputerguide.com/guides/
@AiComputerGuide
$17/GB VRAM.
Used RTX 3090 (~$407, 24GB) still beats new mid-range cards on pure capacity economics for local 70B Q4.
5070 Ti: ~$59/GB · 5090: ~$41/GB
Capacity loads the model. Bandwidth sets tok/s.
Full breakdown: aicomputerguide.com/
ALT VRAM price per GB comparison: used RTX 3090 at $17/GB, RTX 5070 Ti at $59/GB, RTX 5090 at $41/GB for local LLMs