Hmm… I can’t say anything too certain about the actual performance, but if we’re talking about how I’d compare them:
I think the most useful number here is not really 30 seats, but your stated ~3–6 concurrent requests, together with the actual model/quantization and RAG prompt lengths.
My rough answers to the four questions would be:
-
A single machine is a very reasonable place to start for 3–6 concurrent requests, but I would not assume either Spark or Mac Studio will stay comfortable just from the hardware specs. A 32B model with moderate prompts and a 70B model with long document contexts are very different serving workloads.
-
I would not call vLLM’s advantage “insurmountable”. It is a meaningful advantage in serving maturity and scheduling, but Apple Silicon is no longer limited to a simple single-request MLX loop: llama.cpp’s server has parallel slots and continuous batching, and there is now a vllm-metal backend as well. On the other hand, Spark currently has a broader and more established serving ecosystem around vLLM / TensorRT-LLM / SGLang / llama.cpp.
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For 1× Mac Studio vs 2× Mac mini, I would first separate two completely different uses of the second machine:
- replication / data parallelism: each machine holds a full copy of the model and different requests go to different machines;
- model splitting / pipeline parallelism: one request/model is divided across both machines.
If the model already fits comfortably on one mini, the first option is usually the more natural way to address a concurrency bottleneck. If the model does not fit on one machine, then the second option becomes relevant.
-
For day-2 scaling, Spark currently looks less experimental if distributed-serving maturity is the main priority. Multi-Mac serving is nevertheless becoming quite real: current vllm-metal distributed docs document both two-Mac pipeline parallelism over Thunderbolt and two-Mac data-parallel replicas. The important caveat is that the docs explicitly describe multi-Mac support as new and currently validated only for a fairly narrow set of configurations.
So my default path would probably be:
start with one machine, measure the real 1 / 3 / 6-user workload, and only then decide whether the second machine needs to add request throughput or model capacity.
That distinction probably matters more than “one big machine vs two small machines” by itself.
A small test matrix like this would tell you much more than comparing memory-bandwidth numbers:
| Test |
What I would measure |
| Same model + quant, 1 request |
single-user baseline |
| Same model + quant, 3 concurrent |
realistic normal peak |
| Same model + quant, 6 concurrent |
stated worst-case-ish peak |
| Short chat prompt |
decode-heavy behavior |
| Real representative RAG/document prompt |
prefill + KV-cache behavior |
| Cold prefix / repeated shared prefix |
effect of prompt/prefix caching |
| Metrics |
TTFT, per-user decode rate/TPOT, p95/p99 end-to-end latency, aggregate throughput, memory/KV headroom, errors/stalls |
vLLM’s benchmark tooling already reports TTFT/TPOT/ITL/end-to-end percentiles, and GuideLLM is also useful if you want to drive different OpenAI-compatible servers with the same client-side load profile. It can do fixed concurrency as well as burstier/Poisson-style request arrival.
Why I would not decide from memory bandwidth alone
There are really at least two different performance phases in a typical LLM request:
- prefill: processing the input prompt/document;
- decode: generating output tokens one by one.
A long RAG/document-analysis request can spend a substantial amount of time in prefill, while an interactive chat with a short prompt may be dominated by decode.
There is a particularly useful near-apples-to-apples example in StepFun’s Step3.5-Flash llama.cpp deployment notes.
They test the same Q4_K_S GGUF model using llama.cpp on both a Mac Studio M4 Max and DGX Spark. It is only a single-stream benchmark, so I would not use it as evidence for your 3–6-user question, and it is not the same model class you necessarily intend to deploy.
But it illustrates the important part nicely: in that particular test the Mac has substantially faster token generation, while Spark catches up and then becomes faster on sufficiently large prompt-prefill workloads.
That is a good example of why statements like:
“800 GB/s vs 273 GB/s, therefore machine A is X times faster”
are too simple for this workload.
The serving engine, model architecture, quantization kernels, prompt length, KV layout, batching level, and whether you are measuring prefill or decode can all change the result.
A useful two-stage comparison, if both machines were actually available, would be:
Stage A: hardware-ish comparison
Use the same GGUF, same llama.cpp revision, same prompt/output lengths, and the same client load on both machines.
NVIDIA actually provides DGX Spark llama.cpp deployment/benchmark material, so this is practical.
Stage B: deployment comparison
Then allow each machine to use its strongest realistic serving stack:
- Spark: vLLM / TensorRT-LLM / SGLang / llama.cpp
- Mac: vllm-metal / llama.cpp / MLX-based serving
Stage A answers something closer to “what is the hardware/runtime backend doing?”
Stage B answers the more useful business question: “what throughput and latency can I actually deploy on this platform?”
The 64 GB / 128 GB question is also more than model-weight size
I would leave fairly generous memory headroom rather than asking only whether the quantized weights technically load.
As a rough sanity check, weight storage alone is approximately:
parameters × bits-per-weight / 8
So 70B at FP8 is already roughly 70 GB before runtime overhead, caches, etc. Q4 is much smaller, but the exact format adds some overhead, and then you still have KV cache and runtime allocations.
For concurrent RAG requests, KV-cache capacity can become much more important than the weights-only number suggests.
That means a machine which can technically start a 70B Q4 model may still be a poor 6-user long-context server.
This is one reason I would choose the context lengths from actual documents rather than benchmarking only a small synthetic prompt.
If embeddings, reranking, OCR/document parsing, or the vector DB will share the same machine, I would also leave those running during a final capacity test. Otherwise the inference benchmark can overstate how much memory/CPU headroom the production box really has.
Mac serving is no longer just 'MLX vs CUDA'
I think this part has changed enough recently that older comparisons can be misleading.
llama.cpp
The current llama.cpp HTTP server supports:
- multiple parallel server slots;
- multi-user parallel decoding;
- continuous/dynamic batching;
- prompt/KV-cache related controls;
- OpenAI-compatible APIs.
So Apple Silicon can certainly perform batched multi-user serving without requiring CUDA/vLLM.
vllm-metal
There is also now vllm-metal, which brings the vLLM serving model to Apple Silicon.
More interestingly for your “second Mac” question, its distributed serving documentation explicitly separates:
Data parallelism (DP)
Each Mac contains the whole model. Requests are load-balanced among independent replicas.
This increases request throughput under concurrency, but does not make a larger model fit.
Pipeline parallelism (PP)
The model is divided into stages across Macs.
This addresses model/capacity splitting, but introduces communication and distributed-runtime concerns.
That is almost exactly the distinction I would use when deciding between one Studio and two minis.
There are meaningful limitations today, though. The vllm-metal docs describe multi-Mac serving as new; the two-machine path has been validated with limited models/configurations, and several combinations such as DP+PP, tensor parallelism, MoE DP, etc. are not generally supported.
So I would treat this as a promising current option, not yet as “two Macs transparently behave like one larger GPU”.
For PP specifically, the current docs use a direct Thunderbolt link for the data plane. I would follow that implementation’s recommended topology rather than assume ordinary Ethernet has equivalent behavior.
mlx_lm.server
mlx_lm.server is convenient for experiments, but its own documentation says it is not recommended for production because it implements only basic security checks.
There is also a recent concurrent prompt-cache deadlock report. I would not interpret one issue as “MLX cannot handle six users”; it is much narrower than that.
I would interpret it as a reason to include a small concurrent soak test and a generation health check before treating that particular server as an unattended internal service.
For DGX Spark, I think the important advantage is the serving ecosystem, not just 'CUDA'
vLLM is a real advantage here, but even Spark’s own vLLM behavior is workload dependent.
The current vLLM DGX Spark guide uses --max-num-seqs 4 in one Nemotron recipe and explains that, for that workload, going beyond four simultaneous decode streams can make the memory-bandwidth cost outweigh continuous-batching gains and sharply worsen TTFT.
That should not be generalized to every model — other models/MoEs can have very different scaling — but it is a useful warning against assuming:
continuous batching = latency stays flat as users are added.
This is exactly why your 3 / 6 concurrency points are worth measuring.
The other benefit is that NVIDIA has fairly extensive DGX Spark playbooks and benchmarking paths for multiple runtimes. Their performance material covers online and offline serving with things such as:
- vLLM
- TensorRT-LLM
- SGLang
- llama.cpp
and there are also official paths for connecting multiple Sparks.
So if your highest priority is “I want the least unusual serving software path, with the broadest CUDA-oriented tooling around it”, I think that is a genuine point in Spark’s favor.
I would just distinguish that from saying it will necessarily beat a high-bandwidth Mac on every model/request shape.
How I would think about 1× Studio vs 2× mini
I would use roughly this decision tree:
Does the target model + useful context + KV headroom fit comfortably on one mini?
├─ Yes
│ │
│ ├─ Is one mini fast enough for 1 user but not for 3–6 users?
│ │ → A second full-model replica is the first two-node design I would test.
│ │
│ └─ Is single-user latency already inadequate?
│ → Two replicas will not fix that.
│ Consider a faster single machine / different model / different quant/runtime.
│
└─ No
│
├─ Does it fit comfortably on one Studio?
│ → One Studio is architecturally much simpler.
│
└─ Does it require memory from both nodes?
→ Now model splitting / PP becomes relevant.
Benchmark communication + failure/restart behavior as part of the system.
This is why I would not treat:
2 × 64 GB mini = one 128 GB accelerator
as the mental model.
Two 64 GB replicas still have 64 GB of usable model capacity per request, but can serve two independent request groups.
A sharded model can use capacity across both nodes, but every request now participates in a distributed execution path.
Those are different products operationally, even if the purchase price happens to be similar.
I also would not make llama.cpp RPC my default production answer for a privacy-sensitive company deployment. The upstream README currently labels that RPC backend proof-of-concept, fragile, and insecure and explicitly warns against using it on an open network or sensitive environment.
That does not make it useless for testing distributed llama.cpp performance, but I would separate “interesting benchmark mechanism” from “the thing carrying private company documents every day”.
A small benchmark I think would be enough before buying around concurrency
I would not turn this into a giant benchmark project.
Something like this would probably have most of the decision value:
Workload A — interactive
Use a realistic short/medium prompt and output length.
Run:
concurrency = 1
concurrency = 3
concurrency = 6
Record:
TTFT
p50/p95/p99 TTFT
TPOT or per-user generation tok/s
p95/p99 end-to-end latency
aggregate output tok/s
peak memory
errors / timeouts / stalls
Workload B — actual RAG/document analysis
Take one of your representative documents after retrieval/prompt assembly and repeat exactly the same 1/3/6 test.
That tells you whether the long prefill changes the ranking.
Workload C — shared-prefix case
If your application has a long common system prompt or repeatedly asks questions over the same document/context, run both:
cold cache
warm/shared prefix
Prefix caching can improve a real workload enormously, but it can also make an artificial benchmark misleading if one system gets cache hits that the real application will not.
Then one short burst
Thirty employees probably will not produce requests at perfectly uniform intervals.
After the fixed-concurrency test, I would send one little burst around the expected peak and watch tail latency.
This is where GuideLLM can be handy: it supports synchronous, fixed-concurrency, throughput and Poisson-style profiles against OpenAI-compatible endpoints.
The important point is not the benchmark tool itself; it is keeping the same request corpus and client-side load pattern across the candidates.
If I had to reduce all of that to purchase paths
I would roughly think of them like this:
DGX Spark
- strongest default if serving-stack maturity / CUDA ecosystem / conventional scale-out is the priority;
- 128 GB gives useful capacity;
- do not assume its lower headline memory bandwidth means poor RAG serving, or that vLLM makes concurrency free.
One large Mac Studio
- attractive if a large amount of unified memory and strong single-node performance let you avoid distributed inference entirely;
- probably the cleanest Apple topology for models that would otherwise require two minis;
- serving-stack choice deserves its own validation rather than treating “Metal” as one runtime.
One Mac mini first
- sensible low-CapEx pilot if the models you actually intend to validate fit with realistic KV/context headroom;
- less useful as a pilot if the eventual workload really requires a much larger 70B/FP8/long-context configuration that cannot be represented on it.
Two Mac minis
- potentially quite sensible as two replicas if each mini can hold the target model;
- much less obviously equivalent to a Studio if the intent is to combine their memory into one distributed model.
One final timing wrinkle: the Apple side of the comparison is unusually time-sensitive right now. Apple has already announced the new M5 Ultra Mac Studio, with up to 512 GB unified memory and 1.2 TB/s memory bandwidth, with general availability scheduled for September 22. Since that is very close, I would at least update the Mac side of the comparison before placing the order. The advertised performance numbers are still vendor claims until independent tests land, though.
So if it were my decision, I would not try to predict a winner from the spec sheet.
I would pick one or two representative models and quantizations, use the real RAG prompt size, measure 1 / 3 / 6 concurrent requests, and first answer this question:
Is the bottleneck model capacity, single-request latency, or aggregate concurrent throughput?
Once you know that, the hardware decision becomes much less ambiguous:
- capacity problem → larger single node or model splitting;
- single-request latency problem → faster single-node path / model/runtime choice;
- concurrency-only problem → replicas become very attractive.
That also gives you a benchmark you can repeat when the second machine is added, instead of having to guess whether the scale-out actually bought what you wanted.