ALoDLM 8B — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
ALoDLM-8B is Amazon's 8-billion-parameter diffusion language model, initialized from a Qwen3 backbone. Rather than generating one token at a time, it produces text and code by repeatedly applying shared transformer layers, keeping the latent state of unresolved tokens and adaptively deciding how much computation each position gets. The training data covers math problems and solutions, programming tasks and instruction-formatted text, and the card reports 83.2 on GSM8K for the 8B model in its comparison table. It is a research release that needs the project's own inference code. The card says the optimized 8B path was checked on an A100 with 40 GB, and a portable CPU decoder is also provided. The model configuration lists a context length of 40,960 tokens, and the card notes that long-context performance beyond the evaluated settings has not been established. It is released under CC-BY-NC-4.0, which allows non-commercial use only. Published in October 2026, it is the larger of two ALoDLM sizes, alongside ALoDLM-1.7B.
497 downloads
14 likesSpecifications
- Publisher
- Amazon
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-10-02
- License
- CC BY-NC 4.0
Get Started
HuggingFace
Run in cloud
Fits on RTX 3060 12GB (6 GB headroom) · Q4_K_M
- Generation speed
- ~42 tok/s
- generation speed
- Cost per 1M output tokens
- $0.40
- per 1M output tokens
How Much VRAM Does ALoDLM 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.1 GB | 9.8 GB | 3.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.2 GB | 9.9 GB | 3.58 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.6 GB | 10.3 GB | 3.99 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.7 GB | 10.4 GB | 4.10 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.5 GB | 11.3 GB | 4.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.4 GB | 12.2 GB | 5.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.4 GB | 13.1 GB | 6.76 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | 14.5 GB | 8.19 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run ALoDLM 8B?
Q4_K_M · 5.5 GBALoDLM 8B (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 41K context window can add up to 5.7 GB, bringing total usage to 11.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run ALoDLM 8B?
Q4_K_M · 5.5 GB53 devices with unified memory can run ALoDLM 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin Nano 8GB (Super).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download ALoDLM 8B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does ALoDLM 8B need?
ALoDLM 8B requires 5.5 GB of VRAM at Q4_K_M, or 17.0 GB at BF16. Full 41K context adds up to 5.7 GB (11.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 6.4 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M5.5 GBQ4_K_M + full context11.3 GB- What's the best quantization for ALoDLM 8B?
For ALoDLM 8B, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.9 GB.
VRAM requirement by quantization
IQ2_XXS2.9 GBIQ3_XS4.0 GBQ4_04.7 GBIQ4_NL5.2 GBQ4_K_M ★5.5 GBBF1617.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run ALoDLM 8B on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run ALoDLM 8B is MacBook Air 13" M3 (8 GB) at IQ2_XXS; 39 of the 39 Macs we list can run it at some quantization. For Q4_K_M (5.5 GB) you need a Mac with more unified memory.
- Can I run ALoDLM 8B locally?
Yes — ALoDLM 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is ALoDLM 8B?
At Q4_K_M, ALoDLM 8B can reach ~870 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~119 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.52 × 0.65 = ~942 tok/s
Estimated speed at Q4_K_M (5.5 GB)
~942 tok/s~119 tok/s~942 tok/s~870 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of ALoDLM 8B?
At Q4_K_M, the download is about 4.91 GB. The full-precision BF16 version is 16.38 GB. The smallest option (IQ2_XXS) is 2.25 GB.
- Which GPUs can run ALoDLM 8B?
52 consumer GPUs can run ALoDLM 8B at Q4_K_M (5.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run ALoDLM 8B?
53 devices with unified memory can run ALoDLM 8B at Q4_K_M (5.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.