128GB vs 256GB: Which LLMs Actually Fit on RTX Spark and Mac Studio M5 Ultra?

Calculation Methodology

Local LLM inference requires model weights to reside entirely in unified memory. Model size ≈ parameter count × quantization bytes. The 2026 desktop standard is 4-bit quantization (Q4/NVFP4) ≈ 0.5 bytes/parameter. Adding context KV cache and system overhead increases actual usage by 10–30% over raw weights.

70B model Q4 ≈ 35GB

125B model Q4 ≈ 63GB

744B model Q4 ≈ 372GB

A reference table (image) summarizes mainstream model capacity accounting.

Model Fit by Tier

Easily fit (<100GB)

Qwen3.8 27B (dense) : ~14GB — both machines have ample headroom for large context.

Llama 70B / Qwen3.8-72B class (dense Q4) : ~42GB — both fit; this is the watershed that consumer 24GB GPUs cannot cross but these two desktop units handle comfortably.

Fit but tight (100–150GB)

Qwen3.8 125B MoE : ~63GB — both single units fit. Prior testing on M5 Ultra 256GB achieved 154.5 tok/s; RTX Spark 128GB also fits but bandwidth becomes constrained (to be examined in next article).

DeepSeek V4 Flash (284B MoE / 13B active) : ~142GB — the dividing line . M5 Ultra 256GB single unit just fits; RTX Spark 128GB single unit falls short and requires dual-unit clustering via ConnectX-7 200Gbps to form a 256GB pool.

Single unit cannot fit (>256GB)

GLM-5.3 744B MoE : ~372GB — neither single unit fits. M5 Ultra would need the 512GB top-tier configuration; RTX Spark requires a 4-unit cluster (~512GB pool). Community testing shows GLM-5.2 744B on 4-Spark achieves ~42 tok/s @1M context.

DeepSeek V4 Pro (1.6T) and larger: beyond even the clustered configurations, belonging to datacenter scale.

Key Pivot: RTX Spark Can Cluster

Each RTX Spark unit includes a ConnectX-7 200Gbps NIC ; two units can be directly connected via QSFP cables to create a 256GB machine, and 3–4 unit RoCE clusters are common in the community — 4 units ≈ 512GB, enabling 744B-class models.

In contrast, the M5 Ultra's single-unit ceiling is 512GB (top-tier, available late October, pricing unannounced) , and it cannot cluster — Apple's unified memory architecture lacks an official multi-node pooling solution comparable to NVLink/ConnectX.

Conclusion

Single unit : M5 Ultra 256GB wins outright (256 vs 128GB, with 512GB top-tier purchasable).

Targeting 744B-class : Spark can reach it via stacking (at the cost of multi-node ops); M5 Ultra must buy the 512GB top-tier.

If your workload stays within 70B dense or 125B MoE , both single units suffice — capacity is not the deciding factor; speed will be the focus of the next article. If you aim for 284B+ or 744B , capacity becomes a hard gate: M5 Ultra 256GB single unit can touch 284B, Spark single unit cannot; but Spark can reach 744B via 4-unit cluster, while M5 Ultra must wait for the 512GB top-tier.