Local LLM deployment: should you choose DeepSeek or GLM?

Before buying a server for your company’s AI, ask what happens when ten people use it at once.

A model answering a question on one developer’s computer tells you very little about that. Your team will bring longer documents, different permissions, messy inputs, and deadlines.

Local LLM deployment means running a large language model, software that interprets and generates language, on hardware you control. For an in-house, on-premises setup, that hardware sits at your premises. Self-hosting in a rented cloud account is a separate option with a different infrastructure boundary.

DeepSeek and Z.ai’s GLM deserve a place on a serious shortlist. They offer downloadable models, documented local deployment paths, and published evaluations. But the right choice depends on the exact model version, available memory, and work you need it to do.

My recommendation: include DeepSeek-V4.1-Flash and GLM-5.3-Flash in a pilot for demanding document or automation tasks when you have the infrastructure budget and someone to operate the system. For a smaller pilot, compare smaller model versions, including GLM-4.7-Flash and Qwen3.8-27B. Test them on your own work before committing to hardware.

This is a research-based deployment guide, checked on October 8, 2026. The published benchmarks discussed below are vendor results, not measurements from a TRS deployment.

Why DeepSeek and GLM are worth considering

The attraction is control. Downloadable weights, the learned numerical values that make a model work, let you run it on your own infrastructure. This processing is called inference. You can also retain a tested model version, or checkpoint, while evaluating an upgrade. You still need to check its license and build the surrounding application.

The current families also cover different needs. Comparing “DeepSeek” with “GLM” without naming a version hides enormous differences in size and capability.

Sources: DeepSeek-V4.1-Flash model card, GLM-5.3-Flash model card, GLM-5.3 model card, and GLM-4.7-Flash model card.

DeepSeek reports 74.2% resolved on DeepSWE v1.1, a software-engineering evaluation, and 90.6% pass@1 on Terminal-Bench 2.1, a terminal-task evaluation, for V4.1-Flash at maximum reasoning effort. Its own results also show that changing the agent software around the model changes performance. These are reasons to investigate it, rather than promises about your company’s tasks. DeepSeek evaluation details

GLM-5.3-Flash combines image and text processing with documented deployment options. The vLLM deployment guide records a test on four H200 GPUs, specialist processors used for AI. That example shows the scale of one tested configuration; your team still needs to size its own workload. vLLM deployment recipe

Are they the best open-source LLMs for business?

They are credible candidates. There is no defensible universal winner for every business workflow and hardware budget.

“Open-weight” is the more precise umbrella term here. It means the model’s learned parameters are available. The Open Source Initiative’s AI definition also addresses code, data information, and the freedoms needed to study and modify a system. A downloadable checkpoint alone does not settle that question. OSI definition

Licenses differ even within GLM. GLM-5.3-Flash lists MIT, while GLM-5.3 has a custom license with an additional security-review condition for certain very large model-as-a-service businesses. Review the exact checkpoint’s terms before deployment. GLM-5.3 license

For tighter memory budgets, Qwen3.8-27B is a relevant comparison: its official card describes a 27B language model with a vision encoder and lists Apache 2.0. Google’s Gemma family also provides models designed for local use. Include these alternatives when their size and capabilities fit the job. Qwen model card, Gemma overview

Local LLM hardware requirements: start with memory

The first sizing mistake is confusing active parameters with the full model.

Parameters are the numerical values stored in a model. A mixture-of-experts model, often shortened to MoE, uses selected parts of the model for each token, a small unit of text. That can reduce computation. It does not mean the rest of the weights disappear. The system must store them and make them available, whether in accelerator memory or through an offloading arrangement.

For a rough first estimate:

Weight storage in bytes ≈ total parameter count × bits per parameter ÷ 8

These are illustrative calculations using hypothetical uniform precision. They are not measured checkpoint sizes or recommended GPU capacities:

GB here means decimal gigabytes. Actual files can use mixed precision and include scales, metadata, or additional components. Quantization reduces numerical precision to save memory, and the resulting model must be evaluated again.

You also need memory for the conversation state, known as the KV cache, plus working buffers and serving overhead. Long documents and simultaneous requests can push a setup past its comfortable limit. vLLM documents how insufficient cache space can cause requests to be interrupted and recomputed, increasing latency. vLLM memory and performance guidance

Specify how much text each request must hold, called its context length, and how many requests must run at once before asking for a hardware quote. Include system RAM, storage for model versions, power, cooling, and the connection between GPUs when applicable. A machine that loads the model still has to finish work quickly enough.

The setup challenges that appear after the first demo

Several users change the performance question

An internal assistant may seem fast until a few colleagues submit long documents together.

Measure time until the first output, total time to complete the task, queue time, and failures under load. For an AI agent, software that uses a model to take steps in a workflow, also measure retries and actions taken in connected tools. A model producing more tokens per second can still take longer to complete useful work.

My proposed pilot would replay the same representative tasks at one, five, and ten concurrent requests, adjusted to the business’s expected traffic. Those are test points, not capacity claims.

The serving software must support the exact checkpoint

Ask your technical team to verify that the software running the model supports the exact model version, hardware, and required features. Image processing and actions in connected tools need testing alongside ordinary text responses.

Ollama can help with supported local models; its documentation explains how to inspect whether a model runs on CPU, GPU, or both. Larger deployments may use a supported vLLM or SGLang configuration. Verify the model-specific recipe instead of assuming any model works with any engine. Ollama FAQ, GLM serving recipe

DeepSeek explicitly describes its V4.1-Flash minimal inference code as a reference implementation rather than a production serving engine. That distinction belongs in a deployment estimate. DeepSeek inference documentation

Internal documents need preparation and permissions

An internal assistant needs a reliable way to find approved information. Retrieval-augmented generation, or RAG, supplies relevant document passages to the model when someone asks a question.

Poor scans, outdated policies, and missing permissions become application problems. A model cannot reliably repair a document search system that gives it the wrong material.

For example, a person who cannot open payroll records should not receive those records through the assistant. OWASP recommends carrying access controls through to document chunks and checking permissions during retrieval, before information reaches the model. OWASP RAG security guidance

Keeping the model local does not keep every integration local

A workflow can use a local model while sending document text to an external scanning service, search provider, or logging platform.

Trace the whole path: uploaded files, text extraction, search, prompts, outputs, logs, and backups. If the system must work offline, install its required software and models, block outgoing connections, and test the complete workflow.

Ollama documents a local-only mode that disables its cloud features. That setting covers Ollama; connected applications still need their own checks. Ollama local-only configuration

Someone has to own updates and failures

Decide who handles a failed server, an unexpectedly slow queue, and a model update that changes output quality.

Keep a tested model version and application configuration available for rollback. Re-run the evaluation when the model, quantization, prompts, retrieval system, or serving engine changes. For workflows that affect business records, define when the system must stop and ask a person to review the result.

What does a self-hosted LLM really cost?

I would build the comparison around completed, accepted tasks. A cheap answer that requires ten minutes of correction is expensive work.

Use the same accounting period for both options:

Local cost = hardware cost spread over its useful life + power and cooling + infrastructure + setup and operations + human review

Cost per accepted task = total cost ÷ tasks completed to the agreed standard

For a hosted model accessed through an API, the connection your application uses to request answers, include model usage, application infrastructure, integration, operations, and review too. Count retry traffic and reasoning tokens where the provider bills for them. Compare options that meet the same quality and data-handling requirements.

A quiet server still consumes capital and support time. A busy server may need spare capacity for peaks and failures. There is no single query count at which self-hosting becomes cheaper for every business.

How I would choose between DeepSeek and GLM

I would start with one workflow, such as answering questions from approved operating procedures or drafting a structured record from a document.

In my work on Drawlytics, improving an AI pipeline for engineering drawings involved reducing missed details and improving detection accuracy. That experience makes me look closely at what the system misses. I would use that same attention to errors when testing a local model on a new workflow.

For a first evaluation, I suggest 50 representative tasks plus 20 difficult or failure cases. This is a practical starting set, not a statistically conclusive benchmark. Keep some cases out of prompt tuning so the final comparison has unseen work.

Use identical inputs and scoring rules. Ask the technical team to record the exact model and software versions, memory-saving settings, hardware, context length, and reasoning settings. Compare models within similar response-time or cost limits as well as at maximum capability.

Choose the smallest configuration that meets the agreed standard. Move to a larger DeepSeek or GLM deployment when its measured improvement justifies the extra infrastructure.

Questions to settle before buying a server

Can DeepSeek or GLM run on a normal office computer?

Some smaller checkpoints or quantized models can be practical on suitable local hardware. The large releases discussed here require a very different capacity plan. Check the exact model and runtime, then measure memory and response time with your intended workload.

Do we need to fine-tune a model on company documents?

For a first document-search pilot, I would start with retrieval and a measured baseline. Consider fine-tuning when testing identifies a specific behavior or task that it could improve. It adds another training, evaluation, and maintenance process.

Does on-premises AI automatically make our data private?

It gives you control over where model inference runs. Privacy also depends on permissions, connected services, logs, backups, and how the application handles its output.

Which should we test first?

For a capable private deployment with sufficient infrastructure, include DeepSeek-V4.1-Flash and GLM-5.3-Flash. For a constrained pilot, start with smaller candidates and compare accepted results per unit of cost. The answer should come from your test set.

At True Refined Solutions, I help businesses turn AI workflows into software their teams can use. If you are considering an in-house LLM, I can help define the pilot, evaluate the deployment options, and identify the operating work before you commit to hardware.

Discuss my in-house AI workflow with TRS