Can you run ALoDLM 8B locally?

ALoDLM family · 2026 · 8.2B parameters · Hugging Face model card

How much VRAM does ALoDLM 8B need at each quantization?

ALoDLM 8B needs 5.4 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.

Diffusion language model: generates text by iterative denoising with a shared recurrent transformer core rather than standard left-to-right autoregression.

Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (36 layers, 8 KV heads, head dim). Source: HF-verified 2026-10-12 via plain api (gated=false safetensors.total=8190735360 params=8.19B ctx=40960 license=cc-by-nc-4.0 repo=amazon/ALoDLM-8B@2c4623d93e285bea86a940c2801eac4509862edd layers=36 hidden=4096 kv_heads=8 heads=32 vocab=151936). MoE: num_experts=None num_experts_per_tok=None active_params_b=None (dense model - no active-parameter split; params from HF safetensors.total). VRAM figures are documented calculations (Q4-class ~0.6 GB per B params, on active params for MoE rows), not measurements. release_date is the HF repo lastModified date observed on the verification pass. No tok/s or benchmark scores invented.

Which GPUs can run ALoDLM 8B locally?

At Q4_K_M with a 4k context, ALoDLM 8B needs 5.4 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.

Runs ALoDLM 8B comfortably (25%+ VRAM headroom)

Needs 2+ GPUs to run ALoDLM 8B

One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 5.4 GB requirement. See our multi-GPU guide for setup.

Can a Mac run ALoDLM 8B?

Yes — these Apple Silicon machines fit ALoDLM 8B at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.

Can ALoDLM 8B run on mini PCs, Jetson, or NPU devices?

These edge and NPU devices from our database have enough memory for ALoDLM 8B at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.

Which pre-built systems can run ALoDLM 8B?

These mini PCs and workstations from our database fit ALoDLM 8B at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.

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