Best VLMs for 128GB on DGX Spark and M4 Mac

Qwen3-VL-32B is the best vision-language model for both platforms. Released in September 2025, this dense 32B-parameter model outperforms the older Qwen2.5-VL-72B on all 15 comparable benchmarks — including a +25-point gain on MathVision and +14 points on MMMU-Pro — while requiring less than half the memory. This makes the traditional “biggest model that fits” calculus obsolete: a well-quantized 32B model now dominates a 72B predecessor. The story diverges sharply between platforms, however, because the M4 Max delivers 2× the memory bandwidth of DGX Spark (546 vs 273 GB/s), making Apple Silicon surprisingly faster for decode-bound VLM inference despite NVIDIA’s superior compute throughput.

The VLM landscape shifted in late 2025

The 2025 generation of VLMs introduced two paradigm shifts. First, MoE architectures went mainstream — Llama 4 Scout packs 109B parameters with only 17B active, and Qwen3-VL-235B uses 22B of its 235B total. Second, training improvements made smaller dense models outperform larger predecessors outright. Qwen3-VL-32B exemplifies this: it beats Qwen2.5-VL-72B on DocVQA (96.9 vs 96.4), MathVista (83.8 vs 74.8), OCRBench-V2 (67.4 vs 61.5), and ScreenSpot (95.8 vs 87.1), among others.

For a 128GB memory budget, the practical candidates are:

VLMs carry extra overhead beyond model weights: each high-resolution image generates 256–4,096 visual tokens, inflating KV cache demands. Budget at least 15–20 GB beyond weights for single-image inference, or 30+ GB for multi-image and video workloads.

NVIDIA DGX Spark: compute-rich but bandwidth-starved

The DGX Spark pairs a Blackwell GPU with 20-core ARM Grace CPU through NVLink-C2C, sharing 128 GB of LPDDR5X in a unified address space. The GPU sees all 128 GB directly — no PCIe bottleneck, no separate VRAM pool. Blackwell’s 5th-gen tensor cores deliver 1 PFLOP at FP4 with native support for NVIDIA’s proprietary NVFP4 format, which compresses weights to ~0.56 bytes per parameter with less than 1% accuracy loss versus FP8.

The critical constraint is 273 GB/s memory bandwidth, shared between CPU and GPU. This is only one-third of an RTX 5090’s bandwidth and half of an M4 Max. Since token generation is memory-bandwidth-bound, decode speed for large models tops out around 2–5 tok/s for 70B+ models and 15–25 tok/s for 32B models. Prefill (the compute-bound phase) is where Blackwell shines — Qwen2.5-VL-7B at NVFP4 achieves 65,832 tok/s prefill via TensorRT-LLM.

Top 3 VLMs for DGX Spark, ranked

#1: Qwen3-VL-32B at NVFP4 (~20 GB weights) The clear winner. At NVFP4, this model consumes roughly 20 GB, leaving 100+ GB for KV cache — enough for its full 262K-token context window with multi-image inputs. Decode speed should reach 15–25 tok/s based on extrapolation from NVIDIA’s published benchmarks of similar-sized models. It dominates Qwen2.5-VL-72B on every vision benchmark while running 3–4× faster. TensorRT-LLM, vLLM, and SGLang all support Qwen3-VL on DGX Spark with official containers. The Apache 2.0 license is a bonus for commercial use. Memory efficiency: excellent. Benchmark quality: best-in-class for the size. Usability: production-viable at interactive speeds.

#2: Qwen2.5-VL-72B at NVFP4 (~40 GB weights) The proven workhorse. Despite being surpassed by its successor on benchmarks, the 72B model remains the most battle-tested large VLM with mature tooling, extensive community support, and demonstrated reliability on document understanding (96.4 DocVQA) and multilingual OCR across 29 languages. At NVFP4, it fits with ~85 GB headroom. Expect ~5–8 tok/s decode, which is usable for batch processing but sluggish for interactive use. NVIDIA has published official playbooks for Qwen2.5-VL on Spark, though only for the 7B variant. Run via vLLM with AWQ-Marlin kernels, which community reports suggest are the fastest quantization path on Spark. Memory efficiency: good. Benchmark quality: strong. Usability: adequate for batch workloads, slow for interactive.

#3: Llama 4 Scout at NVFP4 (~61 GB weights) The MoE wildcard. Scout’s 109B total parameters compress to ~61 GB at NVFP4, and since only 17B parameters activate per token, decode throughput should be closer to a 17B model despite the memory footprint of a 109B model — likely ~20–30 tok/s. Its 10-million-token context window is unmatched. However, independent evaluations reveal significant gaps between Meta’s self-reported benchmarks and real-world performance. Vision capabilities are adequate for basic OCR and chart reading but fall short of Qwen-family models on complex visual reasoning. The LMArena controversy and MATH-Perturb contamination evidence (18% gap between original and perturbed problems) warrant caution. Memory efficiency: good (MoE decode advantage). Benchmark quality: questionable in practice. Usability: fast decode but unreliable vision quality.

Honorable mention: InternVL3-78B at NVFP4 (~44 GB) scores 72.2 MMMU and 906 OCRBench, edging Qwen2.5-VL-72B on reasoning benchmarks. However, its Spark-specific tooling is less mature than the Qwen ecosystem. Gemma 3 27B at BF16 (~54 GB) runs at full precision with fast decode, making it ideal for quick prototyping.

Apple M4 Max MacBook: the bandwidth advantage

The M4 Max with 128 GB unified LPDDR5X delivers 546 GB/s memory bandwidth — exactly double the DGX Spark’s 273 GB/s. Since LLM/VLM decode is almost purely bandwidth-bound, a 70B model at 4-bit quantization generates tokens roughly twice as fast on the Mac as on DGX Spark. The 40-core GPU handles Metal-accelerated inference through MLX (Apple’s native ML framework) or llama.cpp with Metal backend. No M4 Ultra has shipped as of early 2026 — the M4 Max is the highest-end M4 silicon available.

The framework landscape is mature. MLX via mlx-vlm is the gold standard: purpose-built for Apple Silicon VLMs, it runs 20–30% faster than llama.cpp and supports all major architectures including Qwen2.5-VL, Qwen3-VL, Gemma 3, and InternVL. The mlx-community on HuggingFace hosts pre-quantized 4-bit and 8-bit versions of virtually every major VLM. For GGUF enthusiasts, llama.cpp added multimodal support (via libmtmd) in April 2025 and Ollama now natively supports vision models including Gemma 3, Llama 4 Scout, and Qwen-VL variants. LM Studio provides a GUI wrapper around both MLX and llama.cpp backends.

The key limitation: Apple Silicon lacks FP8 and NVFP4 hardware support. Quantization options are standard 4-bit (Q4_K_M in GGUF, 4-bit in MLX) and 8-bit (Q8_0 in GGUF, 8-bit in MLX). Q4_K_M preserves roughly 95% of FP16 quality; 8-bit is near-lossless at ~99.8%.

Top 3 VLMs for M4 Max 128GB, ranked

#1: Qwen3-VL-32B at 8-bit MLX (~34 GB weights) The optimal choice by a wide margin. At 8-bit quantization (near-lossless), the model fits in just 34 GB, leaving ~80 GB for KV cache and OS overhead — enough for massive context windows, multiple images, or even video analysis. MLX delivers an estimated 25–40 tok/s decode, making it fully interactive. Available as mlx-community/Qwen3-VL-32B-Instruct-8bit on HuggingFace. The 262K-token context window, Apache 2.0 license, and state-of-the-art benchmark scores make this the definitive recommendation. You could even run it at BF16 (~64 GB) for maximum quality and still have headroom — a luxury neither the Qwen2.5-VL-72B nor InternVL3-78B can offer. Memory efficiency: exceptional. Benchmark quality: best available. Usability: fast, interactive, well-supported in MLX.

#2: Qwen2.5-VL-72B at 4-bit MLX (~42 GB weights) For users who want the largest dense VLM possible, the 72B model at 4-bit quantization delivers strong absolute performance on document understanding and OCR tasks. Available as mlx-community/Qwen2.5-VL-72B-Instruct-4bit. With 546 GB/s bandwidth, expect ~12–18 tok/s decode — slower than Qwen3-VL-32B but usable for single-query workflows. The 4-bit quantization introduces some quality degradation versus 8-bit, but the model’s sheer scale partially compensates. This is the right choice if you specifically need the 72B model’s proven reliability on production OCR pipelines or its 29-language multilingual capability. At Q8, this model would need ~72 GB — still feasible but leaving limited headroom. Memory efficiency: good. Benchmark quality: strong (but inferior to Qwen3-VL-32B). Usability: adequate speed, mature ecosystem.

#3: Gemma 3 27B at BF16 via MLX (~54 GB weights) The full-precision option. Running at BF16 means zero quantization loss, and the 27B model is fast — expect ~30–45 tok/s decode. Google’s QAT int4 variant drops to just 14 GB with minimal quality loss if speed is paramount. Gemma 3’s 128K context window, 140+ language support, and commercial-friendly license make it a practical all-rounder. It lags 5–10 points behind the 70B class on vision benchmarks (64.9 MMMU vs Qwen3-VL-32B’s 65.3 MMMU-Pro, 90.4 DocVQA vs 96.9), but for many use cases — quick image captioning, basic document parsing, visual Q&A — the speed advantage matters more than the benchmark gap. Memory efficiency: excellent at any precision. Benchmark quality: good for size class. Usability: fastest interactive experience.

Honorable mention: Llama 4 Scout at Q4_K_M (~55–60 GB) runs well via Ollama with fast decode (~25–35 tok/s effective, since only 17B activates), but its vision quality disappoints in practice. MiniCPM-V 4.5 (8B, ~16 GB at BF16) claims to surpass GPT-4o on OpenCompass composite scores and is remarkable for its size — ideal as a secondary model running alongside a larger primary VLM.

The platform tradeoff comes down to bandwidth versus compute

The counterintuitive finding is that the M4 Max is the better inference platform for these models in most scenarios. Its 546 GB/s bandwidth translates directly to faster token generation — roughly 2× the DGX Spark for identical model sizes. The DGX Spark’s advantage lies in prefill throughput (the compute-bound first pass over long prompts), where Blackwell’s 1 PFLOP FP4 tensor cores dominate, and in NVFP4 quantization, which achieves better quality-per-bit than standard 4-bit quantization available on Apple Silicon.

For interactive VLM use — asking questions about images, processing documents one at a time — the M4 Max wins on response speed. For batch processing thousands of images with long prompts, the DGX Spark’s compute advantage and NVIDIA’s mature batching infrastructure (vLLM continuous batching, TensorRT-LLM) provide higher aggregate throughput. The DGX Spark also uniquely enables fine-tuning VLMs locally via NVIDIA’s official playbooks, something not practical on Apple Silicon for models above ~8B parameters.

Both platforms converge on the same answer: Qwen3-VL-32B is the best VLM for 128 GB in early 2026. It delivers benchmark scores that surpass models twice its size, fits comfortably at high precision on either platform, and runs at interactive speeds. The era of needing 70B+ parameters for frontier-class vision understanding has ended.

Conclusion

Three insights emerge from this analysis. First, model architecture improvements have outpaced scaling — Qwen3-VL-32B’s comprehensive victory over Qwen2.5-VL-72B means the “best model that fits” is no longer the largest model that fits. Second, memory bandwidth matters more than compute for local inference, giving the M4 Max a surprising edge over DGX Spark for interactive workloads despite NVIDIA’s superior GPU. Third, MoE models like Llama 4 Scout promise more than they deliver for vision tasks — the benchmark-to-reality gap is wide enough that dense models remain more reliable for production vision workloads. For either platform with 128 GB, run Qwen3-VL-32B at the highest precision your use case allows, and you’ll have state-of-the-art multimodal AI that rivals cloud APIs — entirely offline.