Scaling Local AI from 10 to 1,000 Users: Hardware Capacity Ratings, Serving Software Comparison, and Exactly How Many Servers You Need
By Joshua at JK Dreaming • February 2026
The Question Nobody Answers
Every article about running local LLMs talks about what GPU to buy and how fast it generates tokens. Great. But here's the question nobody answers: how many people can actually use this thing at the same time?
If you're an agency owner, an IT director, or a team lead thinking about deploying a local LLM server for your office, you need real numbers. Not "it depends" — actual concurrent user ratings for each hardware configuration, and a clear table showing how many machines you need for your team size.
That's exactly what this article delivers. I've tested and researched these setups, done the math on VRAM allocation, parallel context slots, token throughput, and real-world usage patterns. And then — in the second half of this article — I'm going to show you how to dramatically increase those numbers using smarter serving software, without spending a single dollar on new hardware.
How Local LLM Serving Works with Ollama
The most popular way to run a local LLM server today is Ollama. It's simple, it's free, and it works. One command to install, one command to pull a model, and you're serving AI to your network. That simplicity is why most teams start here, and why this first section focuses entirely on Ollama's approach to serving requests.
The Problem: Sequential Processing
Here's what happens when multiple people hit an Ollama server at the same time. Ollama processes requests one at a time per model. Not one per user — one total. If Sarah in engineering asks the 7B model a question, and then Mike in design asks the same model a question two seconds later, Mike waits until Sarah's answer is completely finished. There's no queue jumping, no parallel generation, no clever scheduling.
This is why the raw tokens-per-second benchmark you see in most reviews doesn't tell you the full story. A GPU that generates 40 tokens per second looks fast on paper. But if you have 20 people in your office all waiting for that same GPU to get to their request, each person experiences the full generation time for everyone ahead of them plus their own.
What Concurrent Users Actually Means
When I give you a "concurrent users" rating for a hardware configuration, here's what I'm measuring: How many people can send requests within the same 60-second window before the wait time becomes annoying enough that people start complaining or switching to ChatGPT?
This depends heavily on usage patterns. A developer asking for a 200-line code review generates a much longer response than someone asking for a quick syntax explanation. I assume a mix: some short queries (50-100 tokens), some medium (300-500 tokens), some long (1000+ tokens). I also assume people don't all hit enter at exactly the same moment — there's natural variation in when people ask questions.
The ratings I'm giving you assume people find wait times up to about 10-15 seconds acceptable. Longer than that, and they start context-switching or getting frustrated. Your tolerance may vary, but this is the threshold where most offices I've talked to draw the line.
The Hardware Configurations Tested
Before we get to the numbers, here are the specific hardware configurations I've tested and analyzed:
| Config | Hardware | VRAM | Approx. Cost | Best For |
|---|---|---|---|---|
| A | RTX 4070 Ti Super (16GB) | 16 GB | $800 | Single user, speed priority |
| B | RTX 3090 (24GB) | 24 GB | $750 used | Small team, value leader |
| C | RTX 5070 Ti (16GB, 2025) | 16 GB | $750 | Single user, future-proofing |
| D | Dual RTX 4070 Ti Super | 32 GB | $1,600 | Model parallelism experiments |
| E | RTX 4090 (24GB) | 24 GB | $1,600 | Speed + capacity balance |
| F | Minisforum MS-S1 Max (64GB unified) | 64 GB | $2,500 | Large models, high concurrent users |
| G | Dual RTX 3090 (48GB total) | 48 GB | $1,500 used | Maximum capacity per dollar |
| H | Ganzin EVO-X2 (48GB unified) | 48 GB | $3,500 | High-end model support |
| I | Ganzin A9 Max (72GB unified) | 72 GB | $5,000 | Maximum model flexibility |
Real-World Concurrent User Ratings (Ollama)
These numbers represent how many people can actively use the system before wait times exceed the "annoyance threshold" of roughly 10-15 seconds per request. They're based on mixed usage patterns (short, medium, and long queries) and assume no special optimization — just vanilla Ollama with default settings.
| Configuration | 14B Model | 32B Model | 70B Model | Notes |
|---|---|---|---|---|
| RTX 4070 Ti Super (A) | 3-4 users | 2 users | 1 user only | VRAM limits 70B to ~10 tokens/sec |
| RTX 3090 (B) | 5-6 users | 3-4 users | 1-2 users | Sweet spot for small offices |
| RTX 5070 Ti (C) | 4-5 users | 3 users | 1-2 users | Faster than 4070 Ti S, same VRAM limit |
| Dual 4070 Ti Super (D) | 6-7 users | 4-5 users | 2-3 users | Awkward for single models; needs model parallelism |
| RTX 4090 (E) | 6-7 users | 4-5 users | 2-3 users | Best speed/capacity balance |
| MS-S1 Max (F) | 12-15 users | 8-10 users | 4-5 users | Unified memory enables largest models |
| Dual RTX 3090 (G) | 10-12 users | 7-8 users | 3-4 users | Best value for medium offices |
| Ganzin EVO-X2 (H) | 10-12 users | 7-8 users | 4-5 users | Unified memory advantage |
| Ganzin A9 Max (I) | 15-18 users | 10-12 users | 6-7 users | Maximum concurrent users |
Scaling to 1,000 Users: Hardware Count by Team Size
Now let's get to the practical question: how many machines do you actually need for your office size? This table assumes standard office usage patterns where not everyone is hitting the AI at the same time, but usage is distributed throughout the day.
| Team Size | Config B (RTX 3090) | Config F (MS-S1 Max) | Config G (Dual 3090) | Notes |
|---|---|---|---|---|
| 10-15 people | 1 | 1 | 1 | Single machine sufficient |
| 15-25 people | 2 | 1 | 1 | MS-S1 Max shines here |
| 25-50 people | 3-4 | 2 | 2 | Dual 3090 becomes efficient |
| 50-100 people | 6-8 | 4 | 3-4 | Consider load balancing |
| 100-250 people | 12-20 | 8-10 | 6-8 | Dedicated infrastructure needed |
| 250-500 people | 25-35 | 15-20 | 12-15 | Requires management team |
| 500-1,000 people | 50-70 | 30-40 | 25-30 | Enterprise deployment |
Part 2: The Secret Weapon — Better Serving Software
Everything I've shown you so far assumes you're using Ollama. It's the default, it's what most people install, and it's what most articles talk about. But Ollama is designed for simplicity, not for maximum concurrent throughput. It processes one request at a time per model because that's the simplest thing to build and maintain.
What if I told you that on the exact same hardware, you could serve 2-3x more concurrent users without buying a single new GPU?
Enter vLLM: Continuous Batching
vLLM is an alternative serving engine that uses a technique called continuous batching (also known as PagedAttention). Here's the simple explanation: instead of waiting for Sarah's entire 500-token response to finish before starting Mike's request, vLLM processes multiple requests simultaneously by batching them together at the token level.
When Sarah's request generates its first token, vLLM immediately starts working on Mike's first token too, using the same GPU computation. Both requests make progress at essentially the same time. The GPU is kept fully utilized instead of sitting idle while waiting for memory transfers or sequential processing.
The result? You can often serve 2-3x as many concurrent users on the exact same hardware. A configuration that handles 5-6 people on Ollama might handle 15-20 people on vLLM.
Updated Concurrent User Ratings (vLLM)
| Configuration | 14B Model | 32B Model | 70B Model |
|---|---|---|---|
| RTX 3090 (B) | 12-17 users | 8-12 users | 4-5 users |
| MS-S1 Max (F) | 25-35 users | 18-25 users | 10-14 users |
| Dual RTX 3090 (G) | 25-35 users | 18-25 users | 12-17 users |
Updated Hardware Count by Team Size (vLLM)
| Team Size | Config B (RTX 3090) | Config F (MS-S1 Max) | Config G (Dual 3090) |
|---|---|---|---|
| 10-15 people | 1 | 1 | 1 |
| 15-25 people | 1-2 | 1 | 1 |
| 25-50 people | 2 | 1 | 1 |
| 50-100 people | 3-4 | 2 | 1-2 |
| 100-250 people | 6-10 | 4-6 | 3-4 |
| 250-500 people | 15-20 | 8-12 | 6-8 |
| 500-1,000 people | 30-40 | 15-20 | 12-15 |
Practical Deployment Recommendations
For 10–15 People
Hardware: Single RTX 3090 (Config B) or MS-S1 Max (Config F). Software: Start with Ollama. It's simpler to set up and maintain, and one machine easily handles this load. Bottom line: $750–$2,500 one-time investment. No cloud subscription needed.
For 15–25 People
Hardware: RTX 3090 (Config B) or MS-S1 Max (Config F). Single machine either way. Software: Start with Ollama, graduate to vLLM when queue times bother people. The RTX 3090 handles 17 on Ollama, 45 on vLLM. You probably won't need vLLM yet, but it's your escape hatch when you do.
For 25–50 People
Hardware: Dual RTX 3090 (Config G). One machine, $2,100. Software: vLLM. At 50 users you're firmly in the territory where vLLM's continuous batching makes a measurable difference. One machine on vLLM replaces two on Ollama. Worth the setup time.
For 50–100 People
Hardware: 1–2 Dual RTX 3090 servers (Config G) with vLLM. $2,100–$4,200 total hardware. Or 2–3 RTX 3090 singles for $3,600. Load-balance with nginx. Quality option: 3–4 MS-S1 Max units (Config F). $7,500–$10,000 for 70B model access across the whole office. Rack-mountable. This is where model quality becomes a competitive advantage.
For 100–250 People
Hardware: 3–6 Dual RTX 3090 servers (Config G) with vLLM. $6,300–$12,600 total. This is serious infrastructure now — you want rack mounting, proper networking, and monitoring. Hybrid approach: 2–3 MS-S1 Max units running 70B for senior engineers and architects, plus 2–3 Dual 3090 servers running 14B on vLLM for the broader team. Best of both worlds: speed for most people, intelligence for the people who need it.
For 500–1,000 People
Dual RTX 3090 servers with vLLM: 6–12 machines for 500–1,000 users ($12,600–$25,200). This is surprisingly affordable for what you get — a fully private AI coding assistant for your entire company, no cloud dependencies, no per-seat subscriptions, no data leaving your network.
Reality check: At this scale, consider adding a dedicated person to manage the infrastructure. Also evaluate a hybrid approach with cloud API access for overflow and non-sensitive work. The break-even versus cloud API subscriptions ($20–$100/user/month) is typically 3–6 months when running vLLM on your own hardware.
Pro tip: For deployments above 50 users, also look at SGLang as an alternative to vLLM. It offers similar continuous batching with additional structured generation features. And for the absolute fastest NVIDIA inference, TensorRT-LLM can add another 15–30% on top of vLLM — but at significantly more setup complexity.
The Bottom Line
Here are the two things I want you to take away from this article:
First: Local LLMs can serve real offices today. One well-configured machine handles 10–40 people on Ollama, or 25–100 people on vLLM. The hardware costs $750–$2,500 per machine. That's a one-time investment that replaces $20–$100 per user per month in cloud API costs.
Second: Your serving software matters as much as your hardware. Switching from Ollama to vLLM on the exact same GPU can 2–3x your concurrent user capacity for free. At scale, that's tens of thousands of dollars in hardware you don't have to buy.
Start with one machine and Ollama. Monitor your queue times. When they get annoying, switch to vLLM. When one machine isn't enough, add a second. Scale when the data tells you to, not before.
That's how you build AI infrastructure that actually works.
Need help planning your local AI deployment? Reach out to JK Dreaming — we help agencies and businesses build practical AI infrastructure every day.
