On-Premise Coding Assistant for SMBs: What DGX Spark, ASUS Ascent GX10, and Multi-RTX Setups Actually Deliver

In 2026, coding assistants are no longer a bonus — they’re part of a productive developer workflow. Cursor, GitHub Copilot, Claude Code, Cline, Aider — the tools are mature, the adoption is high. The unpleasant side effect: each of these tools sends source code to cloud APIs outside your own company. Fine for open source projects. For patents, proprietary algorithms, regulated industries, and frankly any mid-sized manufacturer with a competitive moat: an open flank.

The good news: in 2026, local coding assistants are competitive for the first time. Three hardware approaches fit our budget corridor under €10,000 — and run models that match closed-source frontier models on many benchmarks. This piece compares the approaches without marketing fluff and gives an honest recommendation per team profile.

1. Why local instead of Copilot cloud

Three recurring arguments come up in client conversations:

Code confidentiality. Source code is many SMBs’ most valuable asset. Patents, trade secrets, competitive advantages are often encoded directly in it. A coding assistant that sends this code to a US hyperscaler is problematic depending on the regulatory framework — from GDPR through secrecy laws to your own customer agreements.

Reproducibility and model versioning. Cloud models change without warning. What works on Monday may behave differently on Thursday — the provider updated the model weights overnight. With a local setup you control which model hash is in production and can roll back if needed.

Cost control under serious usage. A heavily used coding subscription costs €20–€40 per developer per month. For 15 developers, that’s €3,600–€7,200 per year. A local €8,000 investment amortizes in 12–24 months — after that, marginal usage is free.

What’s not yet a given locally: the last 5% of model quality on very complex multi-step tasks. If you need an architecture sketch for a fully greenfield project, a frontier cloud model is often still a touch better. For refactoring code, generating tests, finding bugs, supplementing pull-request reviews — open models are on par in 2026.

2. The three hardware approaches compared

In the budget corridor under €10,000 (hardware only, excluding operating costs), three approaches stay seriously competitive:

All three handle the most important open-source coder models in usable quantizations. The difference lies in throughput per second, concurrent load (how many parallel requests without latency spikes), footprint, and power.

3. NVIDIA DGX Spark

The DGX Spark is NVIDIA’s first “AI workstation in a box” on the GB10 Grace Blackwell chip. Introduced in 2025, available in the US from about $4,700, in Europe via NVIDIA partners and distributors. Specs that matter for our use case:

• 128 GB unified memory between CPU and GPU — no PCIe bottleneck for large models.
• GB10 Grace Blackwell with 1 PFLOPS FP4 performance, ~500 TFLOPS BF16 performance.
• Compact workstation size, fits under the desk or on a shelf.
• Power draw around 170 W under load — affordable, coolable, quiet.
• Full NVIDIA software stack (CUDA, cuBLAS, TensorRT, NIM).

What runs on it:

• Qwen3-Coder-30B-A3B in Q4_K_M quantization at ~60 tokens/sec. Ideal general-purpose coder for a 1–5-person team.
• DeepSeek-Coder-V2-Lite-16B at ~90 tokens/sec. Great for inline completions, very responsive.
• Qwen3-Coder-480B-A35B in Q4 — possible, at 15–25 tokens/sec. Frontier-level model quality, but too slow for interactive inline completions. Better for refactoring sessions.
• Llama-3.3-70B as a generalist for longer explanations and architecture discussions.

Strengths: compact, efficient, plug-and-play. Weaknesses: less throughput than an RTX 6000 Pro workstation, no easy multi-GPU expansion.

Who benefits: solo developers or small teams (1–5 people) without server infrastructure. Also ideal as a per-senior-developer second device — right under the desk, always available.

4. ASUS Ascent GX10

The ASUS Ascent GX10 uses the same GB10 Grace Blackwell chip as the DGX Spark but is shaped as a classic workstation — tower case, replaceable PSU, standard I/O ports. Advantage for companies with IT standardization: you buy a device that fits the existing workstation lifecycle.

Technically practically identical to the DGX Spark in terms of memory and compute. Differences:

• More expandability: additional M.2 slots for local storage (RAG index right on the device), PCIe slot for network card.
• Standard workstation noise level — slightly louder than the DGX Spark.
• Price slightly higher in the €4,500–€5,500 range, but easier to procure in volume through broad reseller channels.
• Better choice for companies buying hardware via IT procurement rather than direct from NVIDIA.

What runs on it runs identically to the DGX Spark. The choice between DGX Spark and Ascent GX10 is primarily about procurement channel and form factor, not performance.

Who benefits: companies with established workstation procurement and IT standards that demand “tower, not black box.”

5. Multi-RTX 6000 Pro setups

The NVIDIA RTX 6000 Pro (Blackwell generation, GB202) is the professional workstation card NVIDIA positions as the successor to the RTX 6000 Ada. Key spec for our use case: 96 GB GDDR7 VRAM per card — double the RTX 6000 Ada (48 GB).

Setup variants:

1× RTX 6000 Pro in workstation:

• Platform: Threadripper Pro workstation or Xeon-W workstation with €7,000–€8,000 total cost.
• 96 GB VRAM supports models up to ~90B in BF16 or ~180B in INT8 quantization.
• ~600 W under full load.
• Throughput: ~90–120 tokens/sec on a mid-size coder model (Qwen3-Coder-30B) — enough for a 5–15-person team with staggered usage.

2× RTX 6000 Pro in workstation:

• ~€14,000–€18,000 total cost — slightly above our €10,000 budget.
• 192 GB VRAM aggregated. Models like DeepSeek-Coder-V3 (671B MoE) run in INT4 without CPU offload.
• ~1,200 W under full load — power and cooling need serious planning.
• Throughput for 15–30 developers concurrently.

Strengths: maximum model size, highest throughput per token, easy linear scaling via additional cards. Weaknesses: power, noise, heat, significantly larger footprint — no longer an “under the desk” device.

Who benefits: mid-size software shops with 10+ developers, their own server infrastructure, possibly with eval or CI workloads running on the same hardware alongside developer inference.

6. Which models actually run

As of May 2026, these are the production-ready open coding models:

Top tier (large memory requirement):

• Qwen3-Coder-480B-A35B — MoE, ~35B active per token, very strong coding performance. Runs in INT4 on 2× RTX 6000 Pro or with limits on a DGX Spark.
• DeepSeek-Coder-V3 (671B MoE) — comparable top performance. Same hardware demands.

Mid tier (well-usable everywhere):

• Qwen3-Coder-30B-A3B — compact, fast, very good performance for most coding tasks. Default recommendation for DGX Spark / Ascent GX10.
• DeepSeek-Coder-V2-Lite-16B — very responsive, ideal for inline completions.
• Codestral 22B v2 (Mistral) — good for refactoring and code review.

Generalists with coding capability:

• Llama-3.3-70B — strong all-rounder, slightly behind specialized models on pure coding.

Recommended split in a productive setup:

• Inline completion (autocomplete): small, fast model — DeepSeek-Coder-V2-Lite-16B or Qwen3-Coder-30B.
• Chat and refactoring requests: mid-size model — Qwen3-Coder-30B or Codestral 22B v2.
• Complex multi-step reasoning or architecture tasks: large model — Qwen3-Coder-480B-A35B or DeepSeek-Coder-V3 (if hardware supports it).

More background on inference performance and quantization and on QLoRA quantization is covered in dedicated pieces.

7. TCO comparison and recommendation

Over 3 years for a 10-person team:

The honest recommendation — by team profile:

• 1–4 developers, sovereignty focus, no server room: DGX Spark or ASUS GX10 per developer. Full sovereignty, plug-and-play, no setup overhead.
• 5–15 developers, IT infrastructure present: 1× RTX 6000 Pro workstation as shared inference, combined with an OpenAI-compatible API layer (vLLM or TGI). Best TCO, widest model selection. Our clear default recommendation for 2026.
• 15+ developers, high concurrent load: 2× RTX 6000 Pro workstation or step directly to a server setup with H100/H200.

What you should not do: buy an RTX 4090 gaming PC and hope “that’s enough.” 24 GB VRAM works for small models, but for serious coder models the card is too small in 2026. You save €4,000 but lose most of the usable models.

A team that takes this step doesn’t just build a coding assistant. You build the foundation for AI in software engineering in-house — from test generation to code review to your own agent workflows. The hardware is ready in 2026, the models are ready, the tools are ready. What’s usually missing is the decision — and an experienced partner for the setup. If you want to know what fits in your specific case: let’s talk.